Example multiverse implementation: Female hurricanes are deadlier than male hurricanes
Abhraneel Sarma, Northwestern University
Alex Kale, University of Washington
2023-02-13
knit_as_emar()Introduction
In this exploratory multiverse analysis report, we implement a specification curve analysis, to answer the research question: whether hurricanes with more feminine names have caused more deaths compared to hurricanes with more masculine names. The original paper found that hurricanes with more feminine names did cause more deaths. However, this paper led to an intense debate about the proper way to analyse the underlying data, providing an opportunity to assess the extent to which the actual outcome is sensitive to arbitrary decisions in the data analysis process.
A specification curve analysis is in principle similar to a multiverse analysis, where all alternate specifications of a particular analysis asking the same research question are explored. In their study, Simonsohn et al. explore the robustness of the analysis by Jung et al. [https://doi.org/10.1073/pnas.1402786111], which investigated whether hurricanes with female sounding names are more deadlier than hurricanes with more male sounding names. We first begin by loading the dataset which is provided by the library. We then rename some of the variables and perform some data transformations which standardises some of the variables (mean = 0 and standard deviation = 1).
data("hurricane")
# read and process data
hurricane_data <- hurricane %>%
# rename some variables
rename(
year = Year,
name = Name,
dam = NDAM,
death = alldeaths,
female = Gender_MF,
masfem = MasFem,
category = Category,
pressure = Minpressure_Updated_2014,
wind = HighestWindSpeed
) %>%
# create new variables
mutate(
post = ifelse(year>1979, 1, 0),
zdam = scale(dam),
zcat = as.numeric(scale(category)),
zpressure = -scale(pressure),
zwind = as.numeric(scale(wind)),
z3 = as.numeric((zpressure + zcat + zwind) / 3)
)Original analysis
Before we implement the multiverse analysis, we illustrate an
implementation of the original analysis by Jung et al. [https://doi.org/10.1073/pnas.1402786111]. The original
analysis used a negative binomial model, which is suitable for
overdispersed count data. Due to some issues with model fit with the
MASS::glm.nb function (see Note 3: https://github.com/uwdata/boba/tree/master/example/hurricane),
we instead use the simpler poisson regression model which will ensure
that none of the models fail while fitting.
In the original analysis, Jung et al. exclude two hurricanes which caused the highest number of deaths (Katrina and Audrey) as outliers. They transform the variable used the interactions between the 11-point femininity rating and both damages and zpressure respectively, as seen below:
df <- hurricane_data %>%
filter( name != "Katrina" & name != "Audrey" )
fit <- glm(death ~ masfem * zdam + masfem * zpressure, data = df, family = "poisson")
summary(fit)##
## Call:
## glm(formula = death ~ masfem * zdam + masfem * zpressure, family = "poisson",
## data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -14.4855 -3.5404 -2.4125 0.5033 18.5521
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.333437 0.076926 30.334 < 2e-16 ***
## masfem 0.059789 0.010545 5.670 1.43e-08 ***
## zdam 0.439454 0.076209 5.766 8.10e-09 ***
## zpressure 0.143637 0.106263 1.352 0.17647
## masfem:zdam 0.024825 0.009495 2.614 0.00894 **
## masfem:zpressure 0.026602 0.013167 2.020 0.04334 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 4031.9 on 91 degrees of freedom
## Residual deviance: 2133.2 on 86 degrees of freedom
## AIC: 2470.1
##
## Number of Fisher Scoring iterations: 6The results above indicate that the femininity of the name of the
hurricane (masfem) does have a statistically significant
effect on deaths. Below, we visualise the expected number of deaths as
the femininity of the name of the hurricane increases. From this, it
seems to suggest that the most feminine hurricane will likely lead to
0.5 extra deaths on average.
data_grid(df, masfem = seq(1, 11, by = 0.2), zdam, zpressure) %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
ggplot(aes(x = masfem, y = .fitted)) +
stat_lineribbon() +
scale_fill_brewer() +
scale_x_continuous(breaks = seq(1, 11, by = 2)) +
theme_minimal() +
labs(x = "masculine-feminine rating (11 point likert scale)", y = "expected number of deaths")
Multiverse Analysis
To implement a multiverse analysis, we first need to create the multiverse object:
M <- multiverse()Excluding outliers
In the original analysis, the authors exclude two most extreme
observations based on the number of deaths. However, this appears to be
an arbitrary choice, especially considering the use of a negative
binomial regression model, which accounts for long-tailed distribution
of the outcome variable (death). In their implementation,
Simonsohn et al. describe a principled method of excluding outliers
based on extreme observations of death and damages. They consider it
reasonable to exclude up two most extreme hurricanes in terms of death,
and upto three most extreme hurricanes in terms of damages. We implement
these decisions in our multiverse using the following two
parameters:
df <- hurricane_data %>%
filter(TRUE) %>%
filter(TRUE)df <- hurricane_data %>%
filter(TRUE) %>%
filter(!(name %in% c("Sandy")))df <- hurricane_data %>%
filter(TRUE) %>%
filter(!(name %in% c("Sandy", "Andrew")))df <- hurricane_data %>%
filter(TRUE) %>%
filter(!(name %in% c("Sandy", "Andrew", "Donna")))df <- hurricane_data %>%
filter(name != "Katrina") %>%
filter(TRUE)df <- hurricane_data %>%
filter(name != "Katrina") %>%
filter(!(name %in% c("Sandy")))df <- hurricane_data %>%
filter(name != "Katrina") %>%
filter(!(name %in% c("Sandy", "Andrew")))df <- hurricane_data %>%
filter(name != "Katrina") %>%
filter(!(name %in% c("Sandy", "Andrew", "Donna")))df <- hurricane_data %>%
filter(!(name %in% c("Katrina", "Audrey"))) %>%
filter(TRUE)df <- hurricane_data %>%
filter(!(name %in% c("Katrina", "Audrey"))) %>%
filter(!(name %in% c("Sandy")))df <- hurricane_data %>%
filter(!(name %in% c("Katrina", "Audrey"))) %>%
filter(!(name %in% c("Sandy", "Andrew")))df <- hurricane_data %>%
filter(!(name %in% c("Katrina", "Audrey"))) %>%
filter(!(name %in% c("Sandy", "Andrew", "Donna")))df <- hurricane_data %>%
filter(branch(death_outliers,
"no_exclusion" ~ TRUE,
"one_most_extreme_deaths" ~ name != "Katrina",
"two_most_extreme_deaths" ~ ! (name %in% c("Katrina", "Audrey"))
)) %>%
filter(branch(damage_outliers,
"no_exclusion" ~ TRUE,
"one_most_extreme_damage" ~ ! (name %in% c("Sandy")),
"two_most_extreme_damage" ~ ! (name %in% c("Sandy", "Andrew")),
"three_most_extreme_damage" ~ ! (name %in% c("Sandy", "Andrew", "Donna"))
))Identifying independent variables
The next decision involves identifying the appropriate independent
variable for the primary effect — how do we operationalise femininity of
the name of a hurricane. Simonsohn et al. identify two distinct ways.
First, using the 11 point scale that was used in the original analysis;
or second using a binary scale. In our multiverse, this decision is
parameterised by:
df <- df %>%
mutate(femininity = masfem)df <- df %>%
mutate(femininity = female)df <- df %>%
mutate(femininity = masfem)df <- df %>%
mutate(femininity = female)df <- df %>%
mutate(femininity = masfem)df <- df %>%
mutate(femininity = female)df <- df %>%
mutate(femininity = masfem)df <- df %>%
mutate(femininity = female)df <- df %>%
mutate(femininity = masfem)df <- df %>%
mutate(femininity = female)df <- df %>%
mutate(femininity = masfem)df <- df %>%
mutate(femininity = female)df <- df %>%
mutate(femininity = masfem)df <- df %>%
mutate(femininity = female)df <- df %>%
mutate(femininity = masfem)df <- df %>%
mutate(femininity = female)df <- df %>%
mutate(femininity = masfem)df <- df %>%
mutate(femininity = female)df <- df %>%
mutate(femininity = masfem)df <- df %>%
mutate(femininity = female)df <- df %>%
mutate(femininity = masfem)df <- df %>%
mutate(femininity = female)df <- df %>%
mutate(femininity = masfem)df <- df %>%
mutate(femininity = female)df <- df %>%
mutate(
femininity = branch(femininity_calculation,
"masfem" ~ masfem,
"female" ~ female
))The damages follow a long tailed, positive only valued
distribution. Thus, the other decision involved is whether or not to
transform damages, another independent variable:
df = df %>%
mutate(, damage = identity(dam))df = df %>%
mutate(, damage = log(dam))df = df %>%
mutate(, damage = identity(dam))df = df %>%
mutate(, damage = log(dam))df = df %>%
mutate(, damage = identity(dam))df = df %>%
mutate(, damage = log(dam))df = df %>%
mutate(, damage = identity(dam))df = df %>%
mutate(, damage = log(dam))df = df %>%
mutate(, damage = identity(dam))df = df %>%
mutate(, damage = log(dam))df = df %>%
mutate(, damage = identity(dam))df = df %>%
mutate(, damage = log(dam))df = df %>%
mutate(, damage = identity(dam))df = df %>%
mutate(, damage = log(dam))df = df %>%
mutate(, damage = identity(dam))df = df %>%
mutate(, damage = log(dam))df = df %>%
mutate(, damage = identity(dam))df = df %>%
mutate(, damage = log(dam))df = df %>%
mutate(, damage = identity(dam))df = df %>%
mutate(, damage = log(dam))df = df %>%
mutate(, damage = identity(dam))df = df %>%
mutate(, damage = log(dam))df = df %>%
mutate(, damage = identity(dam))df = df %>%
mutate(, damage = log(dam))df = df %>%
mutate(, damage = identity(dam))df = df %>%
mutate(, damage = log(dam))df = df %>%
mutate(, damage = identity(dam))df = df %>%
mutate(, damage = log(dam))df = df %>%
mutate(, damage = identity(dam))df = df %>%
mutate(, damage = log(dam))df = df %>%
mutate(, damage = identity(dam))df = df %>%
mutate(, damage = log(dam))df = df %>%
mutate(, damage = identity(dam))df = df %>%
mutate(, damage = log(dam))df = df %>%
mutate(, damage = identity(dam))df = df %>%
mutate(, damage = log(dam))df = df %>%
mutate(, damage = identity(dam))df = df %>%
mutate(, damage = log(dam))df = df %>%
mutate(, damage = identity(dam))df = df %>%
mutate(, damage = log(dam))df = df %>%
mutate(, damage = identity(dam))df = df %>%
mutate(, damage = log(dam))df = df %>%
mutate(, damage = identity(dam))df = df %>%
mutate(, damage = log(dam))df = df %>%
mutate(, damage = identity(dam))df = df %>%
mutate(, damage = log(dam))df = df %>%
mutate(, damage = identity(dam))df = df %>%
mutate(, damage = log(dam))df = df %>%
mutate(,
damage = branch(damage_transform,
"no_transform" ~ identity(dam),
"log_transform" ~ log(dam)
))Declaring alternative specifications of regression model
The next step is to fit the model. We use a linear model for our
analysis, and log-transform the outcome variable deaths because it is a
long-tailed distribution. Our first decision involves whether we want to
include an interaction between femininity and
damage, which is given by
Another decisions involves whether we should control for the year in
which the hurricane occured, as hurricane detection and preparedness
provisions may likely to have improved over the years. In addition,
there’s a discontinuity in 1979, as prior to 1979 all hurricanes had
male names. This manifests as a decisions with three options: not
controlling for year, controlling for the interaction between year and
damage, and controlling for interaction between whether the hurricane
was post 1979 (post) and damage. These decisions are given
by
fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7739 -0.8214 -0.2298 0.8971 4.1454
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.376e+00 2.948e-01 4.668 1.04e-05 ***
femininity 3.421e-02 4.129e-02 0.829 0.41
damage 4.583e-05 5.256e-06 8.720 1.22e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.142 on 91 degrees of freedom
Multiple R-squared: 0.4608, Adjusted R-squared: 0.449
F-statistic: 38.89 on 2 and 91 DF, p-value: 6.219e-13fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.0411 -0.7835 -0.2199 0.8425 4.1349
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.393e+00 2.947e-01 4.727 8.39e-06 ***
femininity 2.775e-02 4.161e-02 0.667 0.507
damage 5.967e-04 4.839e-04 1.233 0.221
damage:year -2.762e-07 2.426e-07 -1.138 0.258
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.14 on 90 degrees of freedom
Multiple R-squared: 0.4685, Adjusted R-squared: 0.4508
F-statistic: 26.44 on 3 and 90 DF, p-value: 2.353e-12fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3700 -0.7951 -0.2054 0.8867 4.1407
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.422e+00 2.938e-01 4.841 5.34e-06 ***
femininity 2.161e-02 4.171e-02 0.518 0.606
damage 5.990e-05 1.029e-05 5.822 8.83e-08 ***
damage:post -1.756e-05 1.107e-05 -1.586 0.116
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.133 on 90 degrees of freedom
Multiple R-squared: 0.4755, Adjusted R-squared: 0.458
F-statistic: 27.2 on 3 and 90 DF, p-value: 1.304e-12fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7697 -0.8316 -0.2388 0.8811 4.1509
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.413e+00 3.345e-01 4.224 5.72e-05 ***
femininity 2.898e-02 4.698e-02 0.617 0.53890
damage 4.270e-05 1.417e-05 3.014 0.00335 **
femininity:damage 4.248e-07 1.786e-06 0.238 0.81249
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.148 on 90 degrees of freedom
Multiple R-squared: 0.4612, Adjusted R-squared: 0.4432
F-statistic: 25.68 on 3 and 90 DF, p-value: 4.321e-12fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.0806 -0.8054 -0.2238 0.8442 4.1404
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.429e+00 3.343e-01 4.275 4.78e-05 ***
femininity 2.261e-02 4.724e-02 0.479 0.633
damage 5.932e-04 4.867e-04 1.219 0.226
femininity:damage 4.176e-07 1.783e-06 0.234 0.815
damage:year -2.760e-07 2.439e-07 -1.132 0.261
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.146 on 89 degrees of freedom
Multiple R-squared: 0.4688, Adjusted R-squared: 0.4449
F-statistic: 19.64 on 4 and 89 DF, p-value: 1.298e-11fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3676 -0.7910 -0.2054 0.8903 4.1396
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.415e+00 3.318e-01 4.265 4.96e-05 ***
femininity 2.253e-02 4.679e-02 0.482 0.63128
damage 6.057e-05 1.813e-05 3.340 0.00122 **
femininity:damage -8.065e-08 1.801e-06 -0.045 0.96438
damage:post -1.766e-05 1.132e-05 -1.560 0.12237
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.139 on 89 degrees of freedom
Multiple R-squared: 0.4755, Adjusted R-squared: 0.4519
F-statistic: 20.17 on 4 and 89 DF, p-value: 7.485e-12fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3404 -0.8299 -0.0068 0.5712 3.4270
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.39196 0.42820 -3.251 0.00161 **
femininity 0.02624 0.03919 0.669 0.50494
damage 0.44562 0.04576 9.737 9.04e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.083 on 91 degrees of freedom
Multiple R-squared: 0.5153, Adjusted R-squared: 0.5047
F-statistic: 48.38 on 2 and 91 DF, p-value: 4.881e-15fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2783 -0.8634 -0.0316 0.6305 3.4874
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.4024571 0.4308658 -3.255 0.0016 **
femininity 0.0301059 0.0404269 0.745 0.4584
damage -0.1757666 1.4741483 -0.119 0.9054
damage:year 0.0003125 0.0007410 0.422 0.6742
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.088 on 90 degrees of freedom
Multiple R-squared: 0.5163, Adjusted R-squared: 0.5001
F-statistic: 32.02 on 3 and 90 DF, p-value: 3.553e-14fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3304 -0.8398 -0.0092 0.5808 3.4374
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.395944 0.432872 -3.225 0.00176 **
femininity 0.027144 0.040707 0.667 0.50661
damage 0.443941 0.049716 8.930 4.82e-14 ***
damage:post 0.002613 0.029358 0.089 0.92929
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.089 on 90 degrees of freedom
Multiple R-squared: 0.5154, Adjusted R-squared: 0.4992
F-statistic: 31.9 on 3 and 90 DF, p-value: 3.865e-14fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3433 -0.8061 0.0404 0.6091 3.3853
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.25298 0.84232 -0.300 0.76461
femininity -0.16079 0.12561 -1.280 0.20378
damage 0.29328 0.10735 2.732 0.00758 **
femininity:damage 0.02474 0.01580 1.566 0.12087
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.074 on 90 degrees of freedom
Multiple R-squared: 0.5282, Adjusted R-squared: 0.5124
F-statistic: 33.58 on 3 and 90 DF, p-value: 1.171e-14fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2606 -0.7663 0.0242 0.6154 3.4644
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.2306559 0.8464489 -0.272 0.786
femininity -0.1616008 0.1260921 -1.282 0.203
damage -0.5392979 1.4789253 -0.365 0.716
femininity:damage 0.0255254 0.0159183 1.604 0.112
damage:year 0.0004163 0.0007375 0.564 0.574
Residual standard error: 1.078 on 89 degrees of freedom
Multiple R-squared: 0.5299, Adjusted R-squared: 0.5087
F-statistic: 25.08 on 4 and 89 DF, p-value: 6.382e-14fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3165 -0.7780 0.0425 0.6089 3.4126
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.246742 0.847159 -0.291 0.7715
femininity -0.161136 0.126276 -1.276 0.2053
damage 0.286501 0.111542 2.569 0.0119 *
femininity:damage 0.025105 0.015954 1.574 0.1191
damage:post 0.007033 0.029255 0.240 0.8106
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.08 on 89 degrees of freedom
Multiple R-squared: 0.5285, Adjusted R-squared: 0.5073
F-statistic: 24.94 on 4 and 89 DF, p-value: 7.251e-14fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7967 -0.7487 -0.2783 0.9239 4.1428
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.434e+00 2.156e-01 6.650 2.13e-09 ***
femininity 2.386e-01 2.527e-01 0.944 0.348
damage 4.586e-05 5.247e-06 8.740 1.10e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.141 on 91 degrees of freedom
Multiple R-squared: 0.462, Adjusted R-squared: 0.4502
F-statistic: 39.08 on 2 and 91 DF, p-value: 5.618e-13fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.0265 -0.7478 -0.2476 0.8230 4.1316
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.437e+00 2.153e-01 6.673 1.99e-09 ***
femininity 1.983e-01 2.549e-01 0.778 0.438
damage 5.877e-04 4.838e-04 1.215 0.228
damage:year -2.717e-07 2.425e-07 -1.120 0.266
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.139 on 90 degrees of freedom
Multiple R-squared: 0.4694, Adjusted R-squared: 0.4517
F-statistic: 26.54 on 3 and 90 DF, p-value: 2.174e-12fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3530 -0.7644 -0.2290 0.8730 4.1365
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.453e+00 2.143e-01 6.780 1.22e-09 ***
femininity 1.603e-01 2.557e-01 0.627 0.532
damage 5.970e-05 1.028e-05 5.808 9.41e-08 ***
damage:post -1.729e-05 1.108e-05 -1.561 0.122
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.132 on 90 degrees of freedom
Multiple R-squared: 0.4762, Adjusted R-squared: 0.4588
F-statistic: 27.28 on 3 and 90 DF, p-value: 1.226e-12fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7840 -0.7923 -0.2730 0.8890 4.1529
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.478e+00 2.392e-01 6.180 1.83e-08 ***
femininity 1.781e-01 2.891e-01 0.616 0.539510
damage 4.169e-05 1.091e-05 3.819 0.000246 ***
femininity:damage 5.442e-06 1.246e-05 0.437 0.663458
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.146 on 90 degrees of freedom
Multiple R-squared: 0.4632, Adjusted R-squared: 0.4453
F-statistic: 25.88 on 3 and 90 DF, p-value: 3.662e-12fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.0902 -0.7869 -0.2383 0.8088 4.1408
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.476e+00 2.389e-01 6.179 1.9e-08 ***
femininity 1.450e-01 2.904e-01 0.499 0.619
damage 5.759e-04 4.871e-04 1.182 0.240
femininity:damage 4.855e-06 1.246e-05 0.390 0.698
damage:year -2.676e-07 2.439e-07 -1.097 0.276
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.145 on 89 degrees of freedom
Multiple R-squared: 0.4703, Adjusted R-squared: 0.4465
F-statistic: 19.76 on 4 and 89 DF, p-value: 1.147e-11fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3562 -0.7727 -0.2273 0.8698 4.1385
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.461e+00 2.379e-01 6.143 2.22e-08 ***
femininity 1.494e-01 2.878e-01 0.519 0.605055
damage 5.870e-05 1.574e-05 3.730 0.000337 ***
femininity:damage 1.069e-06 1.272e-05 0.084 0.933233
damage:post -1.707e-05 1.145e-05 -1.491 0.139388
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.138 on 89 degrees of freedom
Multiple R-squared: 0.4763, Adjusted R-squared: 0.4527
F-statistic: 20.23 on 4 and 89 DF, p-value: 7.032e-12fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3256 -0.8221 -0.0162 0.5876 3.4571
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.28217 0.38504 -3.330 0.00126 **
femininity 0.08066 0.24148 0.334 0.73914
damage 0.44621 0.04600 9.701 1.08e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.085 on 91 degrees of freedom
Multiple R-squared: 0.5135, Adjusted R-squared: 0.5028
F-statistic: 48.03 on 2 and 91 DF, p-value: 5.772e-15fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2742 -0.8603 -0.0249 0.6336 3.5091
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.2846554 0.3869543 -3.320 0.0013 **
femininity 0.1051797 0.2519314 0.417 0.6773
damage -0.0940736 1.4943910 -0.063 0.9499
damage:year 0.0002716 0.0007509 0.362 0.7184
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.09 on 90 degrees of freedom
Multiple R-squared: 0.5142, Adjusted R-squared: 0.498
F-statistic: 31.76 on 3 and 90 DF, p-value: 4.286e-14fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3230 -0.8256 -0.0161 0.5874 3.4599
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.2828484 0.3881359 -3.305 0.00136 **
femininity 0.0826413 0.2556799 0.323 0.74728
damage 0.4457120 0.0503817 8.847 7.18e-14 ***
damage:post 0.0007433 0.0299840 0.025 0.98028
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.091 on 90 degrees of freedom
Multiple R-squared: 0.5135, Adjusted R-squared: 0.4973
F-statistic: 31.67 on 3 and 90 DF, p-value: 4.571e-14fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3342 -0.7957 0.0148 0.6053 3.4175
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.63652 0.56660 -1.123 0.264
femininity -0.99270 0.73557 -1.350 0.181
damage 0.35627 0.07402 4.813 5.96e-06 ***
femininity:damage 0.14514 0.09403 1.543 0.126
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.077 on 90 degrees of freedom
Multiple R-squared: 0.5261, Adjusted R-squared: 0.5103
F-statistic: 33.3 on 3 and 90 DF, p-value: 1.428e-14fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2635 -0.7675 0.0169 0.6271 3.4882
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.6214653 0.5697617 -1.091 0.278
femininity -0.9895951 0.7386754 -1.340 0.184
damage -0.3914507 1.4942281 -0.262 0.794
femininity:damage 0.1492900 0.0947914 1.575 0.119
damage:year 0.0003746 0.0007476 0.501 0.618
Residual standard error: 1.081 on 89 degrees of freedom
Multiple R-squared: 0.5274, Adjusted R-squared: 0.5062
F-statistic: 24.83 on 4 and 89 DF, p-value: 8.009e-14fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3167 -0.7826 0.0102 0.6161 3.4366
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.634498 0.569807 -1.114 0.268
femininity -0.990241 0.739717 -1.339 0.184
damage 0.351996 0.078636 4.476 2.24e-05 ***
femininity:damage 0.146620 0.094957 1.544 0.126
damage:post 0.005029 0.029885 0.168 0.867
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.083 on 89 degrees of freedom
Multiple R-squared: 0.5262, Adjusted R-squared: 0.5049
F-statistic: 24.71 on 4 and 89 DF, p-value: 8.931e-14fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7737 -0.8308 -0.2148 0.9130 4.1445
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.372e+00 2.969e-01 4.621 1.27e-05 ***
femininity 3.457e-02 4.154e-02 0.832 0.407
damage 4.622e-05 5.562e-06 8.311 9.30e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.148 on 90 degrees of freedom
Multiple R-squared: 0.4391, Adjusted R-squared: 0.4266
F-statistic: 35.23 on 2 and 90 DF, p-value: 5.019e-12fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.0564 -0.7924 -0.2533 0.8538 4.1350
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.397e+00 2.973e-01 4.699 9.46e-06 ***
femininity 2.709e-02 4.201e-02 0.645 0.521
damage 6.274e-04 5.174e-04 1.213 0.228
damage:year -2.917e-07 2.597e-07 -1.123 0.264
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.146 on 89 degrees of freedom
Multiple R-squared: 0.4469, Adjusted R-squared: 0.4283
F-statistic: 23.97 on 3 and 89 DF, p-value: 1.842e-11fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3698 -0.8015 -0.2267 0.9003 4.1410
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.424e+00 2.964e-01 4.805 6.24e-06 ***
femininity 2.133e-02 4.207e-02 0.507 0.613
damage 5.990e-05 1.035e-05 5.790 1.04e-07 ***
damage:post -1.776e-05 1.136e-05 -1.564 0.121
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.139 on 89 degrees of freedom
Multiple R-squared: 0.4541, Adjusted R-squared: 0.4357
F-statistic: 24.68 on 3 and 89 DF, p-value: 1.04e-11fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7693 -0.8359 -0.2179 0.8908 4.1504
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.411e+00 3.364e-01 4.194 6.46e-05 ***
femininity 2.905e-02 4.723e-02 0.615 0.53999
damage 4.294e-05 1.428e-05 3.007 0.00343 **
femininity:damage 4.495e-07 1.798e-06 0.250 0.80316
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.154 on 89 degrees of freedom
Multiple R-squared: 0.4395, Adjusted R-squared: 0.4206
F-statistic: 23.26 on 3 and 89 DF, p-value: 3.313e-11fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.0930 -0.8122 -0.2511 0.8556 4.1402
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.431e+00 3.364e-01 4.255 5.21e-05 ***
femininity 2.222e-02 4.756e-02 0.467 0.641
damage 6.215e-04 5.208e-04 1.193 0.236
femininity:damage 3.996e-07 1.796e-06 0.222 0.824
damage:year -2.903e-07 2.612e-07 -1.111 0.269
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.153 on 88 degrees of freedom
Multiple R-squared: 0.4472, Adjusted R-squared: 0.4221
F-statistic: 17.8 on 4 and 88 DF, p-value: 9.714e-11fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3670 -0.7968 -0.2274 0.9049 4.1397
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.416e+00 3.338e-01 4.242 5.45e-05 ***
femininity 2.242e-02 4.707e-02 0.476 0.63498
damage 6.070e-05 1.830e-05 3.318 0.00132 **
femininity:damage -9.656e-08 1.820e-06 -0.053 0.95780
damage:post -1.788e-05 1.165e-05 -1.535 0.12834
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.145 on 88 degrees of freedom
Multiple R-squared: 0.4541, Adjusted R-squared: 0.4293
F-statistic: 18.3 on 4 and 88 DF, p-value: 5.689e-11fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3210 -0.8340 0.0072 0.5928 3.4522
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.33041 0.43070 -3.089 0.00267 **
femininity 0.02429 0.03916 0.620 0.53669
damage 0.43735 0.04623 9.460 3.8e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.081 on 90 degrees of freedom
Multiple R-squared: 0.5029, Adjusted R-squared: 0.4918
F-statistic: 45.52 on 2 and 90 DF, p-value: 2.191e-14fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2971 -0.8400 -0.0106 0.6136 3.4752
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.3365956 0.4347270 -3.075 0.0028 **
femininity 0.0258772 0.0405764 0.638 0.5253
damage 0.1924124 1.5114623 0.127 0.8990
damage:year 0.0001233 0.0007606 0.162 0.8716
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.087 on 89 degrees of freedom
Multiple R-squared: 0.503, Adjusted R-squared: 0.4863
F-statistic: 30.03 on 3 and 89 DF, p-value: 1.669e-13fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3297 -0.8264 0.0018 0.5816 3.4432
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.326229 0.436329 -3.040 0.00311 **
femininity 0.023454 0.040761 0.575 0.56647
damage 0.438765 0.049830 8.805 9.51e-14 ***
damage:post -0.002335 0.029619 -0.079 0.93734
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.087 on 89 degrees of freedom
Multiple R-squared: 0.5029, Adjusted R-squared: 0.4862
F-statistic: 30.01 on 3 and 89 DF, p-value: 1.686e-13fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3259 -0.8145 0.0182 0.5990 3.4101
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.26721 0.84219 -0.317 0.75178
femininity -0.15125 0.12591 -1.201 0.23287
damage 0.29515 0.10734 2.750 0.00722 **
femininity:damage 0.02325 0.01586 1.466 0.14623
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.074 on 89 degrees of freedom
Multiple R-squared: 0.5146, Adjusted R-squared: 0.4982
F-statistic: 31.45 on 3 and 89 DF, p-value: 5.915e-14fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2776 -0.7675 0.0197 0.6000 3.4557
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.2526571 0.8475970 -0.298 0.766
femininity -0.1524914 0.1266069 -1.204 0.232
damage -0.2053734 1.5249762 -0.135 0.893
femininity:damage 0.0238386 0.0160405 1.486 0.141
damage:year 0.0002502 0.0007603 0.329 0.743
Residual standard error: 1.079 on 88 degrees of freedom
Multiple R-squared: 0.5152, Adjusted R-squared: 0.4932
F-statistic: 23.38 on 4 and 88 DF, p-value: 3.46e-13fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3168 -0.8166 0.0078 0.5915 3.4193
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.264845 0.847408 -0.313 0.7554
femininity -0.151488 0.126657 -1.196 0.2349
damage 0.292762 0.111726 2.620 0.0103 *
femininity:damage 0.023394 0.016047 1.458 0.1485
damage:post 0.002457 0.029617 0.083 0.9341
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.08 on 88 degrees of freedom
Multiple R-squared: 0.5146, Adjusted R-squared: 0.4926
F-statistic: 23.33 on 4 and 88 DF, p-value: 3.636e-13fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7971 -0.7525 -0.2857 0.9493 4.1415
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.429e+00 2.175e-01 6.571 3.17e-09 ***
femininity 2.424e-01 2.545e-01 0.953 0.343
damage 4.629e-05 5.552e-06 8.338 8.15e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.147 on 90 degrees of freedom
Multiple R-squared: 0.4404, Adjusted R-squared: 0.428
F-statistic: 35.42 on 2 and 90 DF, p-value: 4.512e-12fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.0396 -0.7512 -0.2511 0.8285 4.1319
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.440e+00 2.175e-01 6.620 2.63e-09 ***
femininity 1.940e-01 2.580e-01 0.752 0.454
damage 6.136e-04 5.180e-04 1.184 0.239
damage:year -2.848e-07 2.600e-07 -1.095 0.276
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.146 on 89 degrees of freedom
Multiple R-squared: 0.4478, Adjusted R-squared: 0.4292
F-statistic: 24.06 on 3 and 89 DF, p-value: 1.711e-11fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3531 -0.7664 -0.2298 0.8831 4.1367
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.454e+00 2.165e-01 6.716 1.70e-09 ***
femininity 1.586e-01 2.584e-01 0.614 0.541
damage 5.970e-05 1.034e-05 5.776 1.11e-07 ***
damage:post -1.744e-05 1.137e-05 -1.533 0.129
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.138 on 89 degrees of freedom
Multiple R-squared: 0.4548, Adjusted R-squared: 0.4364
F-statistic: 24.75 on 3 and 89 DF, p-value: 9.802e-12fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7828 -0.7934 -0.2670 0.9084 4.1526
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.478e+00 2.404e-01 6.149 2.16e-08 ***
femininity 1.748e-01 2.908e-01 0.601 0.549312
damage 4.169e-05 1.097e-05 3.800 0.000264 ***
femininity:damage 6.211e-06 1.274e-05 0.488 0.627034
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.152 on 89 degrees of freedom
Multiple R-squared: 0.4419, Adjusted R-squared: 0.4231
F-statistic: 23.49 on 3 and 89 DF, p-value: 2.739e-11fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.0940 -0.7873 -0.2411 0.8138 4.1406
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.476e+00 2.403e-01 6.144 2.28e-08 ***
femininity 1.449e-01 2.920e-01 0.496 0.621
damage 5.882e-04 5.252e-04 1.120 0.266
femininity:damage 4.680e-06 1.282e-05 0.365 0.716
damage:year -2.738e-07 2.630e-07 -1.041 0.301
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.151 on 88 degrees of freedom
Multiple R-squared: 0.4487, Adjusted R-squared: 0.4236
F-statistic: 17.9 on 4 and 88 DF, p-value: 8.681e-11fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3559 -0.7733 -0.2294 0.8795 4.1385
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.461e+00 2.392e-01 6.108 2.68e-08 ***
femininity 1.496e-01 2.895e-01 0.517 0.606611
damage 5.883e-05 1.611e-05 3.651 0.000442 ***
femininity:damage 9.304e-07 1.318e-05 0.071 0.943868
damage:post -1.721e-05 1.190e-05 -1.445 0.151914
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.145 on 88 degrees of freedom
Multiple R-squared: 0.4548, Adjusted R-squared: 0.4301
F-statistic: 18.35 on 4 and 88 DF, p-value: 5.366e-11fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3042 -0.8030 -0.0323 0.6234 3.4831
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.22331 0.38758 -3.156 0.00217 **
femininity 0.06521 0.24136 0.270 0.78765
damage 0.43801 0.04644 9.432 4.34e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.083 on 90 degrees of freedom
Multiple R-squared: 0.5012, Adjusted R-squared: 0.4901
F-statistic: 45.21 on 2 and 90 DF, p-value: 2.56e-14fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2916 -0.8067 -0.0294 0.6042 3.4958
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.225e+00 3.902e-01 -3.139 0.0023 **
femininity 7.171e-02 2.534e-01 0.283 0.7779
damage 3.012e-01 1.535e+00 0.196 0.8448
damage:year 6.883e-05 7.721e-04 0.089 0.9292
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.089 on 89 degrees of freedom
Multiple R-squared: 0.5012, Adjusted R-squared: 0.4844
F-statistic: 29.81 on 3 and 89 DF, p-value: 1.961e-13fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3204 -0.8090 -0.0225 0.6009 3.4855
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.217740 0.391316 -3.112 0.0025 **
femininity 0.052201 0.256478 0.204 0.8392
damage 0.440998 0.050440 8.743 1.28e-13 ***
damage:post -0.004747 0.030288 -0.157 0.8758
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.089 on 89 degrees of freedom
Multiple R-squared: 0.5013, Adjusted R-squared: 0.4845
F-statistic: 29.82 on 3 and 89 DF, p-value: 1.945e-13fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3153 -0.7909 0.0279 0.5917 3.4428
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.63652 0.56663 -1.123 0.264
femininity -0.92229 0.73901 -1.248 0.215
damage 0.35627 0.07403 4.813 6.05e-06 ***
femininity:damage 0.13383 0.09472 1.413 0.161
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.077 on 89 degrees of freedom
Multiple R-squared: 0.5121, Adjusted R-squared: 0.4957
F-statistic: 31.14 on 3 and 89 DF, p-value: 7.413e-14fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2785 -0.7481 0.0136 0.6084 3.4791
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.6284221 0.5704660 -1.102 0.274
femininity -0.9254243 0.7430050 -1.246 0.216
damage -0.0458785 1.5452101 -0.030 0.976
femininity:damage 0.1368363 0.0959196 1.427 0.157
damage:year 0.0002015 0.0007732 0.261 0.795
Residual standard error: 1.082 on 88 degrees of freedom
Multiple R-squared: 0.5125, Adjusted R-squared: 0.4903
F-statistic: 23.13 on 4 and 88 DF, p-value: 4.402e-13fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3151 -0.7906 0.0276 0.5919 3.4430
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.6364922 0.5699690 -1.117 0.267
femininity -0.9222850 0.7431990 -1.241 0.218
damage 0.3562133 0.0787766 4.522 1.9e-05 ***
femininity:damage 0.1338539 0.0958815 1.396 0.166
damage:post 0.0000635 0.0303243 0.002 0.998
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.083 on 88 degrees of freedom
Multiple R-squared: 0.5121, Adjusted R-squared: 0.4899
F-statistic: 23.09 on 4 and 88 DF, p-value: 4.551e-13fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7450 -0.8504 -0.2053 0.8609 4.1735
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.430e+00 2.980e-01 4.799 6.39e-06 ***
femininity 2.255e-02 4.216e-02 0.535 0.594
damage 4.963e-05 6.026e-06 8.236 1.42e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.142 on 89 degrees of freedom
Multiple R-squared: 0.4406, Adjusted R-squared: 0.428
F-statistic: 35.05 on 2 and 89 DF, p-value: 5.942e-12fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2883 -0.8261 -0.1901 0.8378 4.1641
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.456e+00 2.984e-01 4.881 4.67e-06 ***
femininity 1.481e-02 4.262e-02 0.348 0.729
damage 6.406e-04 5.143e-04 1.245 0.216
damage:year -2.966e-07 2.582e-07 -1.149 0.254
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.14 on 88 degrees of freedom
Multiple R-squared: 0.4489, Adjusted R-squared: 0.4301
F-statistic: 23.89 on 3 and 88 DF, p-value: 2.115e-11fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3891 -0.8205 -0.1882 0.8765 4.1648
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.463e+00 2.980e-01 4.909 4.17e-06 ***
femininity 1.371e-02 4.255e-02 0.322 0.748
damage 6.052e-05 1.034e-05 5.850 8.23e-08 ***
damage:post -1.500e-05 1.160e-05 -1.293 0.199
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.137 on 88 degrees of freedom
Multiple R-squared: 0.451, Adjusted R-squared: 0.4323
F-statistic: 24.1 on 3 and 88 DF, p-value: 1.783e-11fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7498 -0.7723 -0.2310 0.8905 4.1586
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.199e+00 3.503e-01 3.424 0.000939 ***
femininity 5.163e-02 4.810e-02 1.073 0.286024
damage 7.744e-05 2.316e-05 3.343 0.001217 **
femininity:damage -3.344e-06 2.689e-06 -1.243 0.217020
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.138 on 88 degrees of freedom
Multiple R-squared: 0.4502, Adjusted R-squared: 0.4315
F-statistic: 24.02 on 3 and 88 DF, p-value: 1.895e-11fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2063 -0.8045 -0.2392 0.8295 4.1479
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.215e+00 3.496e-01 3.476 0.000795 ***
femininity 4.494e-02 4.828e-02 0.931 0.354488
damage 7.054e-04 5.147e-04 1.371 0.174014
femininity:damage -3.518e-06 2.686e-06 -1.310 0.193707
damage:year -3.145e-07 2.575e-07 -1.221 0.225235
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.135 on 87 degrees of freedom
Multiple R-squared: 0.4595, Adjusted R-squared: 0.4347
F-statistic: 18.49 on 4 and 87 DF, p-value: 4.989e-11fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3100 -0.8031 -0.2295 0.8468 4.1485
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.220e+00 3.489e-01 3.496 0.000745 ***
femininity 4.410e-02 4.818e-02 0.915 0.362527
damage 9.070e-05 2.499e-05 3.629 0.000479 ***
femininity:damage -3.552e-06 2.680e-06 -1.325 0.188554
damage:post -1.587e-05 1.157e-05 -1.371 0.173755
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.133 on 87 degrees of freedom
Multiple R-squared: 0.4619, Adjusted R-squared: 0.4371
F-statistic: 18.67 on 4 and 87 DF, p-value: 4.142e-11fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3194 -0.8334 -0.0169 0.6006 3.4540
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.32828 0.43271 -3.070 0.00284 **
femininity 0.02736 0.04001 0.684 0.49585
damage 0.43376 0.04723 9.185 1.56e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.086 on 89 degrees of freedom
Multiple R-squared: 0.4939, Adjusted R-squared: 0.4826
F-statistic: 43.43 on 2 and 89 DF, p-value: 6.869e-14fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2973 -0.8410 -0.0310 0.6167 3.4753
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.3340423 0.4368096 -3.054 0.00299 **
femininity 0.0288081 0.0413783 0.696 0.48813
damage 0.2066194 1.5189397 0.136 0.89211
damage:year 0.0001144 0.0007645 0.150 0.88141
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.092 on 88 degrees of freedom
Multiple R-squared: 0.4941, Adjusted R-squared: 0.4768
F-statistic: 28.65 on 3 and 88 DF, p-value: 5.123e-13fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3326 -0.8316 -0.0152 0.5926 3.4414
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.321884 0.438465 -3.015 0.00336 **
femininity 0.026185 0.041445 0.632 0.52916
damage 0.435811 0.050536 8.624 2.44e-13 ***
damage:post -0.003543 0.029890 -0.119 0.90592
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.092 on 88 degrees of freedom
Multiple R-squared: 0.494, Adjusted R-squared: 0.4768
F-statistic: 28.64 on 3 and 88 DF, p-value: 5.144e-13fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3234 -0.7464 0.0091 0.6018 3.4072
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.09655 0.86778 -0.111 0.9117
femininity -0.17239 0.12862 -1.340 0.1836
damage 0.26555 0.11316 2.347 0.0212 *
femininity:damage 0.02688 0.01647 1.633 0.1061
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.076 on 88 degrees of freedom
Multiple R-squared: 0.5088, Adjusted R-squared: 0.4921
F-statistic: 30.39 on 3 and 88 DF, p-value: 1.414e-13fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2749 -0.7715 0.0103 0.6110 3.4529
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0818621 0.8733438 -0.094 0.926
femininity -0.1736525 0.1293318 -1.343 0.183
damage -0.2366707 1.5280647 -0.155 0.877
femininity:damage 0.0274784 0.0166488 1.650 0.102
damage:year 0.0002510 0.0007616 0.330 0.743
Residual standard error: 1.081 on 87 degrees of freedom
Multiple R-squared: 0.5094, Adjusted R-squared: 0.4869
F-statistic: 22.59 on 4 and 87 DF, p-value: 8.132e-13fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3205 -0.7472 0.0079 0.6047 3.4101
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0961687 0.8728692 -0.110 0.9125
femininity -0.1724236 0.1293610 -1.333 0.1860
damage 0.2648647 0.1168569 2.267 0.0259 *
femininity:damage 0.0269207 0.0166274 1.619 0.1091
damage:post 0.0007734 0.0297385 0.026 0.9793
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.082 on 87 degrees of freedom
Multiple R-squared: 0.5088, Adjusted R-squared: 0.4862
F-statistic: 22.53 on 4 and 87 DF, p-value: 8.572e-13fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7673 -0.8029 -0.2246 0.8378 4.1646
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.453e+00 2.170e-01 6.699 1.84e-09 ***
femininity 1.793e-01 2.569e-01 0.698 0.487
damage 4.963e-05 6.001e-06 8.270 1.21e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.14 on 89 degrees of freedom
Multiple R-squared: 0.4418, Adjusted R-squared: 0.4293
F-statistic: 35.23 on 2 and 89 DF, p-value: 5.376e-12fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2736 -0.7824 -0.2112 0.8175 4.1550
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.464e+00 2.169e-01 6.752 1.5e-09 ***
femininity 1.297e-01 2.604e-01 0.498 0.620
damage 6.250e-04 5.150e-04 1.214 0.228
damage:year -2.888e-07 2.585e-07 -1.117 0.267
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.139 on 88 degrees of freedom
Multiple R-squared: 0.4497, Adjusted R-squared: 0.4309
F-statistic: 23.97 on 3 and 88 DF, p-value: 1.986e-11fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3734 -0.7828 -0.2077 0.8603 4.1561
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.470e+00 2.166e-01 6.784 1.30e-09 ***
femininity 1.212e-01 2.602e-01 0.466 0.642
damage 6.024e-05 1.033e-05 5.830 8.98e-08 ***
damage:post -1.464e-05 1.163e-05 -1.259 0.211
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.137 on 88 degrees of freedom
Multiple R-squared: 0.4517, Adjusted R-squared: 0.433
F-statistic: 24.17 on 3 and 88 DF, p-value: 1.686e-11fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7828 -0.7517 -0.2697 0.8629 4.1526
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.357e+00 2.514e-01 5.396 5.7e-07 ***
femininity 2.964e-01 2.993e-01 0.990 0.324646
damage 6.193e-05 1.711e-05 3.620 0.000491 ***
femininity:damage -1.404e-05 1.827e-05 -0.768 0.444465
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.143 on 88 degrees of freedom
Multiple R-squared: 0.4456, Adjusted R-squared: 0.4267
F-statistic: 23.57 on 3 and 88 DF, p-value: 2.739e-11fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2329 -0.7535 -0.2343 0.8173 4.1379
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.340e+00 2.509e-01 5.343 7.23e-07 ***
femininity 2.738e-01 2.987e-01 0.917 0.362
damage 7.323e-04 5.265e-04 1.391 0.168
femininity:damage -1.822e-05 1.850e-05 -0.984 0.328
damage:year -3.347e-07 2.627e-07 -1.274 0.206
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.139 on 87 degrees of freedom
Multiple R-squared: 0.4557, Adjusted R-squared: 0.4307
F-statistic: 18.21 on 4 and 87 DF, p-value: 6.713e-11fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3358 -0.7831 -0.2310 0.8530 4.1388
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.342e+00 2.502e-01 5.366 6.59e-07 ***
femininity 2.694e-01 2.981e-01 0.904 0.368728
damage 7.828e-05 2.053e-05 3.813 0.000256 ***
femininity:damage -1.879e-05 1.847e-05 -1.017 0.311908
damage:post -1.682e-05 1.182e-05 -1.423 0.158448
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.136 on 87 degrees of freedom
Multiple R-squared: 0.4582, Adjusted R-squared: 0.4333
F-statistic: 18.39 on 4 and 87 DF, p-value: 5.545e-11fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3036 -0.8262 -0.0320 0.6307 3.4854
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.21441 0.39031 -3.111 0.0025 **
femininity 0.07995 0.24616 0.325 0.7461
damage 0.43493 0.04748 9.160 1.75e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.088 on 89 degrees of freedom
Multiple R-squared: 0.4919, Adjusted R-squared: 0.4805
F-statistic: 43.08 on 2 and 89 DF, p-value: 8.228e-14fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2921 -0.8282 -0.0288 0.6199 3.4970
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.216e+00 3.930e-01 -3.094 0.00265 **
femininity 8.578e-02 2.579e-01 0.333 0.74024
damage 3.107e-01 1.543e+00 0.201 0.84084
damage:year 6.253e-05 7.761e-04 0.081 0.93597
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.094 on 88 degrees of freedom
Multiple R-squared: 0.4919, Adjusted R-squared: 0.4746
F-statistic: 28.4 on 3 and 88 DF, p-value: 6.16e-13fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3231 -0.8256 -0.0370 0.6181 3.5049
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.207270 0.394289 -3.062 0.00292 **
femininity 0.064963 0.260114 0.250 0.80336
damage 0.438394 0.051190 8.564 3.24e-13 ***
damage:post -0.005723 0.030555 -0.187 0.85184
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.094 on 88 degrees of freedom
Multiple R-squared: 0.4921, Adjusted R-squared: 0.4748
F-statistic: 28.42 on 3 and 88 DF, p-value: 6.074e-13fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3153 -0.8093 0.0123 0.5643 3.4428
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.54721 0.58221 -0.940 0.350
femininity -1.01160 0.75187 -1.345 0.182
damage 0.34007 0.07772 4.376 3.32e-05 ***
femininity:damage 0.15003 0.09774 1.535 0.128
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.08 on 88 degrees of freedom
Multiple R-squared: 0.5051, Adjusted R-squared: 0.4883
F-statistic: 29.94 on 3 and 88 DF, p-value: 1.957e-13fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2780 -0.7580 0.0127 0.5699 3.4796
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.5388357 0.5861718 -0.919 0.361
femininity -1.0149430 0.7559835 -1.343 0.183
damage -0.0675949 1.5499894 -0.044 0.965
femininity:damage 0.1531100 0.0989500 1.547 0.125
damage:year 0.0002042 0.0007755 0.263 0.793
Residual standard error: 1.086 on 87 degrees of freedom
Multiple R-squared: 0.5055, Adjusted R-squared: 0.4828
F-statistic: 22.24 on 4 and 87 DF, p-value: 1.139e-12fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3196 -0.8134 0.0142 0.5673 3.4381
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.547387 0.585550 -0.935 0.352
femininity -1.012054 0.756247 -1.338 0.184
damage 0.341098 0.081888 4.165 7.3e-05 ***
femininity:damage 0.149632 0.098752 1.515 0.133
damage:post -0.001289 0.030473 -0.042 0.966
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.086 on 87 degrees of freedom
Multiple R-squared: 0.5051, Adjusted R-squared: 0.4824
F-statistic: 22.2 on 4 and 87 DF, p-value: 1.177e-12fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.9798 -0.7962 -0.1807 0.8581 4.1661
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.394e+00 2.969e-01 4.696 9.69e-06 ***
femininity 2.571e-02 4.192e-02 0.613 0.541
damage 5.300e-05 6.389e-06 8.295 1.16e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.134 on 88 degrees of freedom
Multiple R-squared: 0.4455, Adjusted R-squared: 0.4329
F-statistic: 35.35 on 2 and 88 DF, p-value: 5.39e-12fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7268 -0.8023 -0.1235 0.7492 4.1323
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.422e+00 2.880e-01 4.935 3.82e-06 ***
femininity 8.925e-03 4.116e-02 0.217 0.82883
damage 1.626e-03 6.112e-04 2.659 0.00932 **
damage:year -7.873e-07 3.060e-07 -2.573 0.01178 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.099 on 87 degrees of freedom
Multiple R-squared: 0.4847, Adjusted R-squared: 0.4669
F-statistic: 27.28 on 3 and 87 DF, p-value: 1.576e-12fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7203 -0.8041 -0.1169 0.7847 4.1338
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.432e+00 2.862e-01 5.004 2.89e-06 ***
femininity 6.991e-03 4.090e-02 0.171 0.86468
damage 8.570e-05 1.314e-05 6.523 4.38e-09 ***
damage:post -3.890e-05 1.381e-05 -2.817 0.00599 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.091 on 87 degrees of freedom
Multiple R-squared: 0.4919, Adjusted R-squared: 0.4743
F-statistic: 28.07 on 3 and 87 DF, p-value: 8.642e-13fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7481 -0.7231 -0.1892 0.9074 4.1532
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.189e+00 3.485e-01 3.413 0.000977 ***
femininity 5.163e-02 4.784e-02 1.079 0.283457
damage 7.776e-05 2.304e-05 3.375 0.001103 **
femininity:damage -3.005e-06 2.686e-06 -1.119 0.266305
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.132 on 87 degrees of freedom
Multiple R-squared: 0.4534, Adjusted R-squared: 0.4345
F-statistic: 24.05 on 3 and 87 DF, p-value: 1.992e-11fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7313 -0.7997 -0.1737 0.7495 4.1194
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.216e+00 3.379e-01 3.600 0.000531 ***
femininity 3.488e-02 4.681e-02 0.745 0.458234
damage 1.651e-03 6.104e-04 2.704 0.008250 **
femininity:damage -3.009e-06 2.603e-06 -1.156 0.250812
damage:year -7.875e-07 3.054e-07 -2.579 0.011621 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.097 on 86 degrees of freedom
Multiple R-squared: 0.4926, Adjusted R-squared: 0.469
F-statistic: 20.87 on 4 and 86 DF, p-value: 4.744e-12fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7247 -0.8088 -0.1457 0.7755 4.1208
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.225e+00 3.355e-01 3.652 0.000446 ***
femininity 3.314e-02 4.649e-02 0.713 0.477881
damage 1.108e-04 2.505e-05 4.422 2.84e-05 ***
femininity:damage -3.035e-06 2.584e-06 -1.174 0.243486
damage:post -3.897e-05 1.378e-05 -2.828 0.005825 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.089 on 86 degrees of freedom
Multiple R-squared: 0.4999, Adjusted R-squared: 0.4766
F-statistic: 21.49 on 4 and 86 DF, p-value: 2.584e-12fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3170 -0.8365 -0.0298 0.6056 3.4572
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.32054 0.43978 -3.003 0.00348 **
femininity 0.02700 0.04035 0.669 0.50507
damage 0.43284 0.04809 9.001 4.09e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.092 on 88 degrees of freedom
Multiple R-squared: 0.4856, Adjusted R-squared: 0.4739
F-statistic: 41.53 on 2 and 88 DF, p-value: 1.988e-13fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2906 -0.8514 -0.0514 0.6259 3.4827
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.3254192 0.4431448 -2.991 0.00362 **
femininity 0.0286012 0.0416340 0.687 0.49393
damage 0.1679877 1.5498234 0.108 0.91393
damage:year 0.0001333 0.0007794 0.171 0.86464
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.098 on 87 degrees of freedom
Multiple R-squared: 0.4857, Adjusted R-squared: 0.468
F-statistic: 27.39 on 3 and 87 DF, p-value: 1.445e-12fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3287 -0.8336 -0.0199 0.6057 3.4450
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.315982 0.444571 -2.960 0.00396 **
femininity 0.026029 0.041707 0.624 0.53421
damage 0.434733 0.051864 8.382 8.25e-13 ***
damage:post -0.003067 0.030405 -0.101 0.91990
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.098 on 87 degrees of freedom
Multiple R-squared: 0.4856, Adjusted R-squared: 0.4679
F-statistic: 27.38 on 3 and 87 DF, p-value: 1.459e-12fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3257 -0.7506 0.0149 0.6030 3.4037
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.09089 0.87403 -0.104 0.9174
femininity -0.17417 0.13024 -1.337 0.1846
damage 0.26465 0.11406 2.320 0.0227 *
femininity:damage 0.02717 0.01674 1.623 0.1082
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.082 on 87 degrees of freedom
Multiple R-squared: 0.5007, Adjusted R-squared: 0.4835
F-statistic: 29.08 on 3 and 87 DF, p-value: 4.066e-13fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2778 -0.7750 0.0205 0.6138 3.4494
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0790043 0.8794106 -0.090 0.929
femininity -0.1746565 0.1309318 -1.334 0.186
damage -0.2214437 1.5534672 -0.143 0.887
femininity:damage 0.0276275 0.0168909 1.636 0.106
damage:year 0.0002431 0.0007749 0.314 0.754
Residual standard error: 1.088 on 86 degrees of freedom
Multiple R-squared: 0.5013, Adjusted R-squared: 0.4781
F-statistic: 21.61 on 4 and 86 DF, p-value: 2.301e-12fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3246 -0.7502 0.0136 0.6045 3.4049
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0908122 0.8791326 -0.103 0.9180
femininity -0.1741624 0.1310009 -1.329 0.1872
damage 0.2643951 0.1175985 2.248 0.0271 *
femininity:damage 0.0271795 0.0168773 1.610 0.1110
damage:post 0.0002964 0.0302027 0.010 0.9922
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.088 on 86 degrees of freedom
Multiple R-squared: 0.5007, Adjusted R-squared: 0.4775
F-statistic: 21.56 on 4 and 86 DF, p-value: 2.414e-12fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.9688 -0.7413 -0.2001 0.8418 4.1588
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.427e+00 2.162e-01 6.600 2.98e-09 ***
femininity 1.957e-01 2.553e-01 0.766 0.445
damage 5.303e-05 6.369e-06 8.325 1.00e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.132 on 88 degrees of freedom
Multiple R-squared: 0.4468, Adjusted R-squared: 0.4343
F-statistic: 35.54 on 2 and 88 DF, p-value: 4.853e-12fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7388 -0.8070 -0.1288 0.7583 4.1262
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.425e+00 2.098e-01 6.793 1.3e-09 ***
femininity 8.090e-02 2.518e-01 0.321 0.7488
damage 1.611e-03 6.132e-04 2.627 0.0102 *
damage:year -7.802e-07 3.070e-07 -2.541 0.0128 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.099 on 87 degrees of freedom
Multiple R-squared: 0.485, Adjusted R-squared: 0.4673
F-statistic: 27.32 on 3 and 87 DF, p-value: 1.533e-12fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7299 -0.8082 -0.1223 0.7974 4.1289
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.434e+00 2.083e-01 6.884 8.57e-10 ***
femininity 6.401e-02 2.506e-01 0.255 0.79895
damage 8.546e-05 1.317e-05 6.490 5.08e-09 ***
damage:post -3.862e-05 1.387e-05 -2.784 0.00659 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.091 on 87 degrees of freedom
Multiple R-squared: 0.4921, Adjusted R-squared: 0.4746
F-statistic: 28.09 on 3 and 87 DF, p-value: 8.49e-13fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7776 -0.7076 -0.2520 0.8806 4.1503
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.357e+00 2.500e-01 5.426 5.13e-07 ***
femininity 2.813e-01 2.978e-01 0.945 0.347418
damage 6.193e-05 1.701e-05 3.640 0.000462 ***
femininity:damage -1.037e-05 1.836e-05 -0.565 0.573542
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.137 on 87 degrees of freedom
Multiple R-squared: 0.4488, Adjusted R-squared: 0.4298
F-statistic: 23.62 on 3 and 87 DF, p-value: 2.836e-11fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7554 -0.8255 -0.1612 0.7483 4.1118
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.317e+00 2.424e-01 5.434 5.07e-07 ***
femininity 2.074e-01 2.896e-01 0.716 0.47576
damage 1.689e-03 6.203e-04 2.724 0.00782 **
femininity:damage -1.590e-05 1.789e-05 -0.888 0.37680
damage:year -8.125e-07 3.095e-07 -2.625 0.01026 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.1 on 86 degrees of freedom
Multiple R-squared: 0.4897, Adjusted R-squared: 0.466
F-statistic: 20.63 on 4 and 86 DF, p-value: 6.013e-12fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7468 -0.8278 -0.1457 0.7580 4.1140
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.323e+00 2.405e-01 5.499 3.85e-07 ***
femininity 1.955e-01 2.877e-01 0.680 0.4986
damage 1.010e-04 2.126e-05 4.751 8.03e-06 ***
femininity:damage -1.657e-05 1.777e-05 -0.933 0.3537
damage:post -4.020e-05 1.399e-05 -2.874 0.0051 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.092 on 86 degrees of freedom
Multiple R-squared: 0.4972, Adjusted R-squared: 0.4738
F-statistic: 21.26 on 4 and 86 DF, p-value: 3.247e-12fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3006 -0.8387 -0.0525 0.6362 3.4891
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.20615 0.39609 -3.045 0.00307 **
femininity 0.07798 0.24785 0.315 0.75378
damage 0.43376 0.04835 8.972 4.7e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.094 on 88 degrees of freedom
Multiple R-squared: 0.4835, Adjusted R-squared: 0.4718
F-statistic: 41.19 on 2 and 88 DF, p-value: 2.365e-13fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2846 -0.8448 -0.0477 0.6320 3.5052
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.207e+00 3.985e-01 -3.030 0.00322 **
femininity 8.570e-02 2.593e-01 0.330 0.74186
damage 2.642e-01 1.575e+00 0.168 0.86719
damage:year 8.527e-05 7.919e-04 0.108 0.91450
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.1 on 87 degrees of freedom
Multiple R-squared: 0.4836, Adjusted R-squared: 0.4658
F-statistic: 27.16 on 3 and 87 DF, p-value: 1.728e-12fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3185 -0.8308 -0.0723 0.6367 3.5076
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.201158 0.399457 -3.007 0.00345 **
femininity 0.064924 0.261581 0.248 0.80457
damage 0.437049 0.052570 8.314 1.14e-12 ***
damage:post -0.005115 0.031103 -0.164 0.86976
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.1 on 87 degrees of freedom
Multiple R-squared: 0.4837, Adjusted R-squared: 0.4659
F-statistic: 27.17 on 3 and 87 DF, p-value: 1.715e-12fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3159 -0.8283 0.0266 0.5693 3.4420
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.54721 0.58554 -0.935 0.353
femininity -1.01384 0.75986 -1.334 0.186
damage 0.34007 0.07816 4.351 3.68e-05 ***
femininity:damage 0.15039 0.09903 1.519 0.132
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.086 on 87 degrees of freedom
Multiple R-squared: 0.4969, Adjusted R-squared: 0.4795
F-statistic: 28.64 on 3 and 87 DF, p-value: 5.642e-13fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2775 -0.7677 0.0132 0.5730 3.4801
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.5387773 0.5895915 -0.914 0.363
femininity -1.0140929 0.7639679 -1.327 0.188
damage -0.0704391 1.5785677 -0.045 0.965
femininity:damage 0.1529910 0.1000626 1.529 0.130
damage:year 0.0002056 0.0007898 0.260 0.795
Residual standard error: 1.092 on 86 degrees of freedom
Multiple R-squared: 0.4973, Adjusted R-squared: 0.4739
F-statistic: 21.27 on 4 and 86 DF, p-value: 3.216e-12fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3209 -0.8318 0.0343 0.5719 3.4365
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.547409 0.588941 -0.929 0.355
femininity -1.014873 0.764575 -1.327 0.188
damage 0.341229 0.082441 4.139 8.1e-05 ***
femininity:damage 0.150025 0.099912 1.502 0.137
damage:post -0.001453 0.030978 -0.047 0.963
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.092 on 86 degrees of freedom
Multiple R-squared: 0.4969, Adjusted R-squared: 0.4735
F-statistic: 21.23 on 4 and 86 DF, p-value: 3.321e-12fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7964 -0.7723 -0.1779 0.8740 4.1528
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.308e+00 3.006e-01 4.352 3.56e-05 ***
femininity 3.864e-02 4.142e-02 0.933 0.353
damage 5.055e-05 6.743e-06 7.498 4.37e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.141 on 90 degrees of freedom
Multiple R-squared: 0.3872, Adjusted R-squared: 0.3736
F-statistic: 28.43 on 2 and 90 DF, p-value: 2.686e-10fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.0505 -0.7823 -0.1833 0.8494 4.1430
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.342e+00 3.055e-01 4.394 3.07e-05 ***
femininity 3.270e-02 4.242e-02 0.771 0.443
damage 4.297e-04 5.484e-04 0.783 0.435
damage:year -1.909e-07 2.761e-07 -0.691 0.491
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.144 on 89 degrees of freedom
Multiple R-squared: 0.3905, Adjusted R-squared: 0.3699
F-statistic: 19.01 on 3 and 89 DF, p-value: 1.304e-09fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3833 -0.7727 -0.1690 0.8780 4.1450
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.382e+00 3.059e-01 4.519 1.91e-05 ***
femininity 2.561e-02 4.268e-02 0.600 0.550
damage 6.013e-05 1.034e-05 5.814 9.40e-08 ***
damage:post -1.498e-05 1.230e-05 -1.218 0.226
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.138 on 89 degrees of freedom
Multiple R-squared: 0.3973, Adjusted R-squared: 0.3769
F-statistic: 19.55 on 3 and 89 DF, p-value: 7.995e-10fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.1236 -0.8262 -0.1684 0.8658 4.1880
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.463e+00 3.337e-01 4.386 3.16e-05 ***
femininity 1.317e-02 4.777e-02 0.276 0.7834
damage 3.672e-05 1.460e-05 2.514 0.0137 *
femininity:damage 2.306e-06 2.159e-06 1.068 0.2883
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.14 on 89 degrees of freedom
Multiple R-squared: 0.395, Adjusted R-squared: 0.3746
F-statistic: 19.37 on 3 and 89 DF, p-value: 9.439e-10fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2532 -0.8159 -0.1981 0.8547 4.1791
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.461e+00 3.354e-01 4.357 3.56e-05 ***
femininity 1.332e-02 4.801e-02 0.277 0.782
damage 2.237e-04 5.984e-04 0.374 0.709
femininity:damage 2.030e-06 2.343e-06 0.866 0.389
damage:year -9.331e-08 2.985e-07 -0.313 0.755
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.146 on 88 degrees of freedom
Multiple R-squared: 0.3956, Adjusted R-squared: 0.3682
F-statistic: 14.4 on 4 and 88 DF, p-value: 4.387e-09fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.4315 -0.8050 -0.2037 0.8362 4.1653
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.444e+00 3.356e-01 4.302 4.37e-05 ***
femininity 1.577e-02 4.802e-02 0.328 0.7433
damage 5.044e-05 2.371e-05 2.127 0.0362 *
femininity:damage 1.198e-06 2.637e-06 0.454 0.6507
damage:post -1.108e-05 1.505e-05 -0.736 0.4638
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.143 on 88 degrees of freedom
Multiple R-squared: 0.3987, Adjusted R-squared: 0.3713
F-statistic: 14.59 on 4 and 88 DF, p-value: 3.542e-09fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2748 -0.8361 -0.0155 0.6810 3.5146
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.15195 0.41217 -2.795 0.00635 **
femininity 0.01396 0.03733 0.374 0.70931
damage 0.41938 0.04408 9.514 2.93e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.027 on 90 degrees of freedom
Multiple R-squared: 0.5036, Adjusted R-squared: 0.4926
F-statistic: 45.66 on 2 and 90 DF, p-value: 2.048e-14fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3048 -0.8316 0.0094 0.6537 3.4858
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.1436217 0.4161594 -2.748 0.00726 **
femininity 0.0118812 0.0387457 0.307 0.75983
damage 0.7274208 1.4248388 0.511 0.61095
damage:year -0.0001551 0.0007170 -0.216 0.82925
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.032 on 89 degrees of freedom
Multiple R-squared: 0.5039, Adjusted R-squared: 0.4872
F-statistic: 30.13 on 3 and 89 DF, p-value: 1.547e-13fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3240 -0.7952 0.0116 0.6206 3.4640
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.125916 0.417627 -2.696 0.00839 **
femininity 0.009045 0.038917 0.232 0.81674
damage 0.427279 0.047334 9.027 3.3e-14 ***
damage:post -0.013304 0.028196 -0.472 0.63821
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.031 on 89 degrees of freedom
Multiple R-squared: 0.5049, Adjusted R-squared: 0.4882
F-statistic: 30.25 on 3 and 89 DF, p-value: 1.418e-13fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2805 -0.7718 0.0576 0.6035 3.4844
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.48657 0.80879 -0.602 0.54897
femininity -0.09696 0.12185 -0.796 0.42832
damage 0.33003 0.10332 3.194 0.00194 **
femininity:damage 0.01477 0.01544 0.956 0.34150
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.027 on 89 degrees of freedom
Multiple R-squared: 0.5087, Adjusted R-squared: 0.4921
F-statistic: 30.71 on 3 and 89 DF, p-value: 1.009e-13fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2929 -0.7626 0.0605 0.6151 3.4728
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.916e-01 8.153e-01 -0.603 0.548
femininity -9.641e-02 1.227e-01 -0.786 0.434
damage 4.592e-01 1.455e+00 0.316 0.753
femininity:damage 1.458e-02 1.567e-02 0.930 0.355
damage:year -6.445e-05 7.241e-04 -0.089 0.929
Residual standard error: 1.033 on 88 degrees of freedom
Multiple R-squared: 0.5087, Adjusted R-squared: 0.4864
F-statistic: 22.78 on 4 and 88 DF, p-value: 6.13e-13fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3166 -0.7684 0.0322 0.5895 3.4484
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.500455 0.813817 -0.615 0.54018
femininity -0.095069 0.122581 -0.776 0.44008
damage 0.340315 0.108028 3.150 0.00223 **
femininity:damage 0.014034 0.015667 0.896 0.37280
damage:post -0.009832 0.028492 -0.345 0.73085
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.032 on 88 degrees of freedom
Multiple R-squared: 0.5093, Adjusted R-squared: 0.487
F-statistic: 22.84 on 4 and 88 DF, p-value: 5.805e-13fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7958 -0.7020 -0.2530 0.8701 4.1544
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.385e+00 2.200e-01 6.294 1.10e-08 ***
femininity 2.540e-01 2.528e-01 1.004 0.318
damage 5.046e-05 6.737e-06 7.490 4.52e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.14 on 90 degrees of freedom
Multiple R-squared: 0.3881, Adjusted R-squared: 0.3745
F-statistic: 28.55 on 2 and 90 DF, p-value: 2.508e-10fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.0341 -0.7205 -0.2083 0.8500 4.1427
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.404e+00 2.223e-01 6.314 1.04e-08 ***
femininity 2.206e-01 2.581e-01 0.855 0.395
damage 4.280e-04 5.463e-04 0.784 0.435
damage:year -1.901e-07 2.750e-07 -0.691 0.491
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.143 on 89 degrees of freedom
Multiple R-squared: 0.3914, Adjusted R-squared: 0.3709
F-statistic: 19.08 on 3 and 89 DF, p-value: 1.22e-09fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3655 -0.7436 -0.2193 0.8615 4.1430
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.426e+00 2.221e-01 6.421 6.44e-09 ***
femininity 1.790e-01 2.597e-01 0.689 0.492
damage 5.996e-05 1.034e-05 5.800 9.96e-08 ***
damage:post -1.482e-05 1.225e-05 -1.209 0.230
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.137 on 89 degrees of freedom
Multiple R-squared: 0.398, Adjusted R-squared: 0.3777
F-statistic: 19.62 on 3 and 89 DF, p-value: 7.557e-10fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.0279 -0.7934 -0.2013 0.8308 4.1864
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.478e+00 2.378e-01 6.216 1.61e-08 ***
femininity 1.027e-01 2.922e-01 0.351 0.726177
damage 4.169e-05 1.085e-05 3.841 0.000229 ***
femininity:damage 1.427e-05 1.384e-05 1.031 0.305377
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.139 on 89 degrees of freedom
Multiple R-squared: 0.3954, Adjusted R-squared: 0.375
F-statistic: 19.4 on 3 and 89 DF, p-value: 9.171e-10fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2042 -0.7882 -0.2000 0.8238 4.1755
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.477e+00 2.390e-01 6.183 1.93e-08 ***
femininity 1.020e-01 2.937e-01 0.347 0.729
damage 2.653e-04 5.795e-04 0.458 0.648
femininity:damage 1.248e-05 1.466e-05 0.852 0.397
damage:year -1.120e-07 2.903e-07 -0.386 0.700
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.145 on 88 degrees of freedom
Multiple R-squared: 0.3964, Adjusted R-squared: 0.3689
F-statistic: 14.45 on 4 and 88 DF, p-value: 4.162e-09fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3985 -0.7786 -0.2019 0.8422 4.1631
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.467e+00 2.387e-01 6.145 2.28e-08 ***
femininity 1.148e-01 2.933e-01 0.391 0.69651
damage 5.294e-05 1.796e-05 2.948 0.00409 **
femininity:damage 7.738e-06 1.616e-05 0.479 0.63319
damage:post -1.129e-05 1.434e-05 -0.788 0.43307
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.142 on 88 degrees of freedom
Multiple R-squared: 0.3996, Adjusted R-squared: 0.3723
F-statistic: 14.64 on 4 and 88 DF, p-value: 3.316e-09fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2657 -0.8278 -0.0038 0.6977 3.5320
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.09066 0.36890 -2.956 0.00397 **
femininity 0.04009 0.22894 0.175 0.86139
damage 0.41953 0.04425 9.480 3.44e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.027 on 90 degrees of freedom
Multiple R-squared: 0.503, Adjusted R-squared: 0.492
F-statistic: 45.55 on 2 and 90 DF, p-value: 2.163e-14fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3009 -0.8008 -0.0119 0.6641 3.4965
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.0860267 0.3712406 -2.925 0.00436 **
femininity 0.0222591 0.2398941 0.093 0.92628
damage 0.7984476 1.4401062 0.554 0.58067
damage:year -0.0001907 0.0007244 -0.263 0.79297
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.033 on 89 degrees of freedom
Multiple R-squared: 0.5034, Adjusted R-squared: 0.4867
F-statistic: 30.07 on 3 and 89 DF, p-value: 1.614e-13fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3172 -0.7629 0.0206 0.6296 3.4752
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.072016 0.372084 -2.881 0.00497 **
femininity -0.001299 0.242962 -0.005 0.99575
damage 0.428918 0.047883 8.958 4.6e-14 ***
damage:post -0.015108 0.028723 -0.526 0.60021
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.031 on 89 degrees of freedom
Multiple R-squared: 0.5046, Adjusted R-squared: 0.4879
F-statistic: 30.21 on 3 and 89 DF, p-value: 1.456e-13fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2748 -0.7232 0.0602 0.6131 3.4999
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.63652 0.53954 -1.180 0.241
femininity -0.72833 0.70529 -1.033 0.305
damage 0.35627 0.07049 5.054 2.29e-06 ***
femininity:damage 0.10417 0.09045 1.152 0.253
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.025 on 89 degrees of freedom
Multiple R-squared: 0.5103, Adjusted R-squared: 0.4938
F-statistic: 30.92 on 3 and 89 DF, p-value: 8.705e-14fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2921 -0.7369 0.0506 0.6082 3.4828
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -6.403e-01 5.433e-01 -1.179 0.242
femininity -7.269e-01 7.093e-01 -1.025 0.308
damage 5.451e-01 1.456e+00 0.374 0.709
femininity:damage 1.028e-01 9.159e-02 1.122 0.265
damage:year -9.458e-05 7.284e-04 -0.130 0.897
Residual standard error: 1.031 on 88 degrees of freedom
Multiple R-squared: 0.5104, Adjusted R-squared: 0.4882
F-statistic: 22.94 on 4 and 88 DF, p-value: 5.281e-13fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3131 -0.7419 0.0328 0.5872 3.4585
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.64108 0.54224 -1.182 0.240
femininity -0.72802 0.70866 -1.027 0.307
damage 0.36593 0.07496 4.882 4.66e-06 ***
femininity:damage 0.09990 0.09153 1.091 0.278
damage:post -0.01137 0.02890 -0.394 0.695
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.03 on 88 degrees of freedom
Multiple R-squared: 0.5112, Adjusted R-squared: 0.489
F-statistic: 23.01 on 4 and 88 DF, p-value: 4.937e-13fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.9364 -0.7960 -0.1618 0.8829 4.1517
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.287e+00 3.040e-01 4.233 5.60e-05 ***
femininity 4.027e-02 4.168e-02 0.966 0.337
damage 5.231e-05 7.438e-06 7.033 3.98e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.145 on 89 degrees of freedom
Multiple R-squared: 0.3592, Adjusted R-squared: 0.3448
F-statistic: 24.95 on 2 and 89 DF, p-value: 2.499e-09fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.0300 -0.7994 -0.1757 0.8713 4.1446
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.325e+00 3.174e-01 4.176 6.96e-05 ***
femininity 3.472e-02 4.369e-02 0.795 0.429
damage 3.492e-04 6.673e-04 0.523 0.602
damage:year -1.499e-07 3.370e-07 -0.445 0.657
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.15 on 88 degrees of freedom
Multiple R-squared: 0.3607, Adjusted R-squared: 0.3389
F-statistic: 16.55 on 3 and 88 DF, p-value: 1.307e-08fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3852 -0.7865 -0.2003 0.8771 4.1451
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.374e+00 3.144e-01 4.371 3.38e-05 ***
femininity 2.657e-02 4.354e-02 0.610 0.543
damage 6.015e-05 1.040e-05 5.783 1.10e-07 ***
damage:post -1.434e-05 1.331e-05 -1.077 0.284
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.144 on 88 degrees of freedom
Multiple R-squared: 0.3676, Adjusted R-squared: 0.346
F-statistic: 17.05 on 3 and 88 DF, p-value: 8.179e-09fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.4526 -0.8216 -0.1678 0.8706 4.1951
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.467e+00 3.340e-01 4.392 3.12e-05 ***
femininity 9.480e-03 4.799e-02 0.198 0.8439
damage 3.616e-05 1.463e-05 2.472 0.0154 *
femininity:damage 2.885e-06 2.253e-06 1.281 0.2037
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.141 on 88 degrees of freedom
Multiple R-squared: 0.371, Adjusted R-squared: 0.3495
F-statistic: 17.3 on 3 and 88 DF, p-value: 6.486e-09fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3301 -0.8342 -0.1419 0.8924 4.2257
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.475e+00 3.356e-01 4.395 3.11e-05 ***
femininity 6.348e-03 4.848e-02 0.131 0.896
damage -4.909e-04 9.182e-04 -0.535 0.594
femininity:damage 4.089e-06 3.084e-06 1.326 0.188
damage:year 2.628e-07 4.578e-07 0.574 0.567
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.145 on 87 degrees of freedom
Multiple R-squared: 0.3733, Adjusted R-squared: 0.3445
F-statistic: 12.96 on 4 and 87 DF, p-value: 2.554e-08fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.4921 -0.8208 -0.1688 0.8614 4.1869
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.460e+00 3.382e-01 4.317 4.17e-05 ***
femininity 1.090e-02 4.900e-02 0.223 0.824
damage 4.049e-05 2.980e-05 1.359 0.178
femininity:damage 2.446e-06 3.471e-06 0.704 0.483
damage:post -3.417e-06 2.046e-05 -0.167 0.868
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.147 on 87 degrees of freedom
Multiple R-squared: 0.3712, Adjusted R-squared: 0.3423
F-statistic: 12.84 on 4 and 87 DF, p-value: 2.953e-08fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2517 -0.8356 -0.0032 0.6625 3.5446
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.07808 0.41360 -2.607 0.0107 *
femininity 0.01153 0.03719 0.310 0.7572
damage 0.40957 0.04443 9.217 1.33e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.021 on 89 degrees of freedom
Multiple R-squared: 0.49, Adjusted R-squared: 0.4785
F-statistic: 42.75 on 2 and 89 DF, p-value: 9.708e-14fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3302 -0.7706 0.0297 0.5876 3.4700
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.0479248 0.4185454 -2.504 0.0141 *
femininity 0.0056692 0.0387254 0.146 0.8839
damage 1.2395133 1.4577542 0.850 0.3975
damage:year -0.0004184 0.0007345 -0.570 0.5704
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.025 on 88 degrees of freedom
Multiple R-squared: 0.4919, Adjusted R-squared: 0.4745
F-statistic: 28.39 on 3 and 88 DF, p-value: 6.19e-13fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3229 -0.7544 0.0388 0.6072 3.4718
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.033538 0.419703 -2.463 0.0157 *
femininity 0.004038 0.038818 0.104 0.9174
damage 0.420526 0.047255 8.899 6.63e-14 ***
damage:post -0.019759 0.028359 -0.697 0.4878
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.024 on 88 degrees of freedom
Multiple R-squared: 0.4928, Adjusted R-squared: 0.4755
F-statistic: 28.5 on 3 and 88 DF, p-value: 5.719e-13fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2581 -0.7772 0.0271 0.6453 3.5167
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.51005 0.80606 -0.633 0.52853
femininity -0.08377 0.12184 -0.688 0.49354
damage 0.33329 0.10297 3.237 0.00171 **
femininity:damage 0.01271 0.01547 0.822 0.41355
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.023 on 88 degrees of freedom
Multiple R-squared: 0.4939, Adjusted R-squared: 0.4766
F-statistic: 28.62 on 3 and 88 DF, p-value: 5.21e-13fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3190 -0.7502 0.0260 0.5873 3.4608
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.5376124 0.8122200 -0.662 0.510
femininity -0.0797778 0.1227431 -0.650 0.517
damage 0.9900512 1.5006045 0.660 0.511
femininity:damage 0.0115655 0.0157605 0.734 0.465
damage:year -0.0003276 0.0007467 -0.439 0.662
Residual standard error: 1.028 on 87 degrees of freedom
Multiple R-squared: 0.495, Adjusted R-squared: 0.4718
F-statistic: 21.32 on 4 and 87 DF, p-value: 2.794e-12fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3170 -0.7473 0.0259 0.5820 3.4588
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.53526 0.81033 -0.661 0.51065
femininity -0.07956 0.12253 -0.649 0.51784
damage 0.35086 0.10779 3.255 0.00162 **
femininity:damage 0.01131 0.01572 0.720 0.47372
damage:post -0.01655 0.02878 -0.575 0.56672
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.027 on 87 degrees of freedom
Multiple R-squared: 0.4958, Adjusted R-squared: 0.4726
F-statistic: 21.39 on 4 and 87 DF, p-value: 2.612e-12fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.9393 -0.7662 -0.2189 0.8842 4.1531
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.366e+00 2.233e-01 6.118 2.48e-08 ***
femininity 2.658e-01 2.546e-01 1.044 0.299
damage 5.226e-05 7.428e-06 7.035 3.94e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.144 on 89 degrees of freedom
Multiple R-squared: 0.3604, Adjusted R-squared: 0.346
F-statistic: 25.07 on 2 and 89 DF, p-value: 2.312e-09fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.0107 -0.7380 -0.2137 0.8739 4.1448
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.390e+00 2.310e-01 6.017 3.98e-08 ***
femininity 2.343e-01 2.659e-01 0.881 0.381
damage 3.402e-04 6.645e-04 0.512 0.610
damage:year -1.454e-07 3.356e-07 -0.433 0.666
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.149 on 88 degrees of freedom
Multiple R-squared: 0.3617, Adjusted R-squared: 0.34
F-statistic: 16.62 on 3 and 88 DF, p-value: 1.218e-08fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3671 -0.7443 -0.2218 0.8755 4.1433
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.419e+00 2.286e-01 6.206 1.73e-08 ***
femininity 1.861e-01 2.653e-01 0.701 0.485
damage 5.998e-05 1.040e-05 5.770 1.16e-07 ***
damage:post -1.407e-05 1.327e-05 -1.061 0.292
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.143 on 88 degrees of freedom
Multiple R-squared: 0.3684, Adjusted R-squared: 0.3469
F-statistic: 17.11 on 3 and 88 DF, p-value: 7.715e-09fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.4218 -0.7900 -0.1663 0.8502 4.1963
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.478e+00 2.377e-01 6.218 1.65e-08 ***
femininity 6.666e-02 2.942e-01 0.227 0.821307
damage 4.169e-05 1.085e-05 3.843 0.000229 ***
femininity:damage 1.976e-05 1.483e-05 1.333 0.186132
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.139 on 88 degrees of freedom
Multiple R-squared: 0.373, Adjusted R-squared: 0.3516
F-statistic: 17.45 on 3 and 88 DF, p-value: 5.635e-09fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2642 -0.8302 -0.1397 0.8629 4.2314
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.480e+00 2.386e-01 6.204 1.81e-08 ***
femininity 4.189e-02 2.979e-01 0.141 0.888
damage -5.263e-04 9.046e-04 -0.582 0.562
femininity:damage 2.834e-05 2.020e-05 1.403 0.164
damage:year 2.845e-07 4.531e-07 0.628 0.532
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.143 on 87 degrees of freedom
Multiple R-squared: 0.3758, Adjusted R-squared: 0.3471
F-statistic: 13.1 on 4 and 87 DF, p-value: 2.16e-08fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.4518 -0.7880 -0.1699 0.8523 4.1910
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.476e+00 2.399e-01 6.153 2.26e-08 ***
femininity 7.247e-02 3.010e-01 0.241 0.8103
damage 4.378e-05 2.269e-05 1.930 0.0569 .
femininity:damage 1.800e-05 2.240e-05 0.804 0.4237
damage:post -2.100e-06 1.996e-05 -0.105 0.9165
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.145 on 87 degrees of freedom
Multiple R-squared: 0.3731, Adjusted R-squared: 0.3443
F-statistic: 12.94 on 4 and 87 DF, p-value: 2.597e-08fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2406 -0.8136 -0.0026 0.6676 3.5625
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.02110 0.37043 -2.757 0.00709 **
femininity 0.02201 0.22815 0.096 0.92337
damage 0.40984 0.04458 9.194 1.49e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.022 on 89 degrees of freedom
Multiple R-squared: 0.4895, Adjusted R-squared: 0.478
F-statistic: 42.67 on 2 and 89 DF, p-value: 1.014e-13fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3243 -0.7529 0.0185 0.5914 3.4789
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.0014701 0.3729848 -2.685 0.00867 **
femininity -0.0240298 0.2402393 -0.100 0.92055
damage 1.3416312 1.4755091 0.909 0.36569
damage:year -0.0004695 0.0007431 -0.632 0.52916
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.025 on 88 degrees of freedom
Multiple R-squared: 0.4918, Adjusted R-squared: 0.4745
F-statistic: 28.39 on 3 and 88 DF, p-value: 6.225e-13fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3139 -0.7354 0.0271 0.5925 3.4820
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.98729 0.37389 -2.641 0.00979 **
femininity -0.04046 0.24273 -0.167 0.86800
damage 0.42274 0.04774 8.855 8.15e-14 ***
damage:post -0.02219 0.02892 -0.767 0.44491
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.024 on 88 degrees of freedom
Multiple R-squared: 0.4929, Adjusted R-squared: 0.4756
F-statistic: 28.51 on 3 and 88 DF, p-value: 5.671e-13fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2507 -0.7331 0.0496 0.6384 3.5321
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.63652 0.53784 -1.183 0.240
femininity -0.63784 0.70679 -0.902 0.369
damage 0.35627 0.07027 5.070 2.18e-06 ***
femininity:damage 0.08967 0.09091 0.986 0.327
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.022 on 88 degrees of freedom
Multiple R-squared: 0.4951, Adjusted R-squared: 0.4779
F-statistic: 28.76 on 3 and 88 DF, p-value: 4.698e-13fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3149 -0.7297 0.0161 0.5889 3.4696
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.6511656 0.5410444 -1.204 0.232
femininity -0.6222977 0.7106129 -0.876 0.384
damage 1.0838902 1.5050115 0.720 0.473
femininity:damage 0.0827051 0.0924381 0.895 0.373
damage:year -0.0003645 0.0007531 -0.484 0.630
Residual standard error: 1.027 on 87 degrees of freedom
Multiple R-squared: 0.4964, Adjusted R-squared: 0.4733
F-statistic: 21.44 on 4 and 87 DF, p-value: 2.474e-12fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3109 -0.7316 0.0201 0.5848 3.4678
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.64395 0.53981 -1.193 0.236
femininity -0.62880 0.70936 -0.886 0.378
damage 0.37199 0.07476 4.976 3.24e-06 ***
femininity:damage 0.08137 0.09216 0.883 0.380
damage:post -0.01851 0.02926 -0.633 0.529
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.026 on 87 degrees of freedom
Multiple R-squared: 0.4974, Adjusted R-squared: 0.4743
F-statistic: 21.52 on 4 and 87 DF, p-value: 2.282e-12fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.4373 -0.7483 -0.1765 0.8389 4.2011
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.320e+00 2.990e-01 4.413 2.88e-05 ***
femininity 2.554e-02 4.155e-02 0.615 0.54
damage 6.162e-05 8.586e-06 7.177 2.14e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.124 on 88 degrees of freedom
Multiple R-squared: 0.3737, Adjusted R-squared: 0.3595
F-statistic: 26.26 on 2 and 88 DF, p-value: 1.14e-09fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3527 -0.7605 -0.1729 0.8472 4.2061
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.303e+00 3.122e-01 4.173 7.09e-05 ***
femininity 2.766e-02 4.309e-02 0.642 0.523
damage -7.738e-05 6.894e-04 -0.112 0.911
damage:year 7.036e-08 3.489e-07 0.202 0.841
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.131 on 87 degrees of freedom
Multiple R-squared: 0.374, Adjusted R-squared: 0.3525
F-statistic: 17.33 on 3 and 87 DF, p-value: 6.601e-09fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.4591 -0.7494 -0.1760 0.8373 4.2001
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.324e+00 3.121e-01 4.242 5.51e-05 ***
femininity 2.502e-02 4.306e-02 0.581 0.563
damage 6.191e-05 1.033e-05 5.991 4.58e-08 ***
damage:post -7.669e-07 1.530e-05 -0.050 0.960
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.131 on 87 degrees of freedom
Multiple R-squared: 0.3738, Adjusted R-squared: 0.3522
F-statistic: 17.31 on 3 and 87 DF, p-value: 6.726e-09fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3390 -0.7368 -0.1883 0.8438 4.1954
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.275e+00 3.517e-01 3.626 0.000485 ***
femininity 3.207e-02 4.957e-02 0.647 0.519302
damage 6.712e-05 2.404e-05 2.792 0.006437 **
femininity:damage -7.800e-07 3.184e-06 -0.245 0.807014
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.131 on 87 degrees of freedom
Multiple R-squared: 0.3742, Adjusted R-squared: 0.3526
F-statistic: 17.34 on 3 and 87 DF, p-value: 6.538e-09fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3298 -0.7427 -0.1847 0.8444 4.1980
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.278e+00 3.583e-01 3.566 0.000594 ***
femininity 3.158e-02 5.100e-02 0.619 0.537356
damage 2.235e-05 9.739e-04 0.023 0.981742
femininity:damage -6.414e-07 4.399e-06 -0.146 0.884408
damage:year 2.216e-08 4.820e-07 0.046 0.963432
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.137 on 86 degrees of freedom
Multiple R-squared: 0.3742, Adjusted R-squared: 0.3451
F-statistic: 12.86 on 4 and 86 DF, p-value: 3.017e-08fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.4027 -0.7632 -0.1963 0.8364 4.1819
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.262e+00 3.568e-01 3.537 0.000655 ***
femininity 3.468e-02 5.072e-02 0.684 0.495976
damage 7.459e-05 3.625e-05 2.058 0.042666 *
femininity:damage -1.544e-06 4.229e-06 -0.365 0.715885
damage:post -5.616e-06 2.031e-05 -0.276 0.782852
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.137 on 86 degrees of freedom
Multiple R-squared: 0.3747, Adjusted R-squared: 0.3457
F-statistic: 12.89 on 4 and 86 DF, p-value: 2.911e-08fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2495 -0.8433 -0.0052 0.6889 3.5472
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.07452 0.41533 -2.587 0.0113 *
femininity 0.01517 0.03796 0.400 0.6903
damage 0.40516 0.04538 8.929 5.75e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.026 on 88 degrees of freedom
Multiple R-squared: 0.4791, Adjusted R-squared: 0.4672
F-statistic: 40.46 on 2 and 88 DF, p-value: 3.453e-13fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3305 -0.7639 0.0254 0.6138 3.4701
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.0432058 0.4202974 -2.482 0.015 *
femininity 0.0092505 0.0394181 0.235 0.815
damage 1.2628509 1.4641646 0.863 0.391
damage:year -0.0004324 0.0007379 -0.586 0.559
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.029 on 87 degrees of freedom
Multiple R-squared: 0.4811, Adjusted R-squared: 0.4632
F-statistic: 26.89 on 3 and 87 DF, p-value: 2.124e-12fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3266 -0.7486 0.0558 0.6038 3.4683
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.025528 0.421431 -2.433 0.017 *
femininity 0.007537 0.039385 0.191 0.849
damage 0.416445 0.047903 8.693 1.91e-13 ***
damage:post -0.021503 0.028607 -0.752 0.454
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.028 on 87 degrees of freedom
Multiple R-squared: 0.4824, Adjusted R-squared: 0.4646
F-statistic: 27.03 on 3 and 87 DF, p-value: 1.905e-12fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2564 -0.7576 0.0034 0.6354 3.5133
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.35728 0.83144 -0.430 0.66847
femininity -0.10300 0.12460 -0.827 0.41068
damage 0.30682 0.10868 2.823 0.00589 **
femininity:damage 0.01601 0.01608 0.996 0.32211
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.026 on 87 degrees of freedom
Multiple R-squared: 0.4849, Adjusted R-squared: 0.4672
F-statistic: 27.3 on 3 and 87 DF, p-value: 1.545e-12fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3162 -0.7331 0.0083 0.5805 3.4584
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.3851874 0.8378804 -0.460 0.647
femininity -0.0989725 0.1255350 -0.788 0.433
damage 0.9526590 1.5049189 0.633 0.528
femininity:damage 0.0148652 0.0163692 0.908 0.366
damage:year -0.0003221 0.0007485 -0.430 0.668
Residual standard error: 1.03 on 86 degrees of freedom
Multiple R-squared: 0.486, Adjusted R-squared: 0.4621
F-statistic: 20.33 on 4 and 86 DF, p-value: 8.129e-12fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3205 -0.7224 0.0188 0.5689 3.4500
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.37699 0.83498 -0.451 0.65277
femininity -0.09939 0.12517 -0.794 0.42936
damage 0.32462 0.11273 2.880 0.00502 **
femininity:damage 0.01465 0.01628 0.900 0.37060
damage:post -0.01803 0.02890 -0.624 0.53437
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.029 on 86 degrees of freedom
Multiple R-squared: 0.4873, Adjusted R-squared: 0.4634
F-statistic: 20.43 on 4 and 86 DF, p-value: 7.361e-12fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.4306 -0.7649 -0.1654 0.8253 4.1969
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.359e+00 2.193e-01 6.199 1.79e-08 ***
femininity 1.843e-01 2.531e-01 0.728 0.469
damage 6.157e-05 8.578e-06 7.178 2.14e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.124 on 88 degrees of freedom
Multiple R-squared: 0.3748, Adjusted R-squared: 0.3606
F-statistic: 26.38 on 2 and 88 DF, p-value: 1.057e-09fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3355 -0.7822 -0.1664 0.8358 4.2030
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.346e+00 2.281e-01 5.903 6.72e-08 ***
femininity 1.982e-01 2.620e-01 0.757 0.451
damage -9.259e-05 6.875e-04 -0.135 0.893
damage:year 7.803e-08 3.479e-07 0.224 0.823
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.13 on 87 degrees of freedom
Multiple R-squared: 0.3752, Adjusted R-squared: 0.3536
F-statistic: 17.41 on 3 and 87 DF, p-value: 6.104e-09fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.4407 -0.7654 -0.1653 0.8246 4.1964
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.361e+00 2.284e-01 5.957 5.31e-08 ***
femininity 1.828e-01 2.622e-01 0.697 0.488
damage 6.170e-05 1.032e-05 5.978 4.87e-08 ***
damage:post -3.521e-07 1.528e-05 -0.023 0.982
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.13 on 87 degrees of freedom
Multiple R-squared: 0.3748, Adjusted R-squared: 0.3533
F-statistic: 17.39 on 3 and 87 DF, p-value: 6.255e-09fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.4218 -0.7627 -0.1666 0.8270 4.1963
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.357e+00 2.485e-01 5.458 4.48e-07 ***
femininity 1.883e-01 3.023e-01 0.623 0.534908
damage 6.193e-05 1.691e-05 3.662 0.000429 ***
femininity:damage -4.879e-07 1.966e-05 -0.025 0.980262
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.13 on 87 degrees of freedom
Multiple R-squared: 0.3748, Adjusted R-squared: 0.3533
F-statistic: 17.39 on 3 and 87 DF, p-value: 6.255e-09fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3514 -0.8108 -0.1558 0.8279 4.2120
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.363e+00 2.509e-01 5.432 5.11e-07 ***
femininity 1.720e-01 3.097e-01 0.555 0.580
damage -1.925e-04 9.287e-04 -0.207 0.836
femininity:damage 4.218e-06 2.619e-05 0.161 0.872
damage:year 1.270e-07 4.636e-07 0.274 0.785
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.136 on 86 degrees of freedom
Multiple R-squared: 0.3754, Adjusted R-squared: 0.3463
F-statistic: 12.92 on 4 and 86 DF, p-value: 2.791e-08fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.4358 -0.7604 -0.1682 0.8279 4.1939
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.356e+00 2.505e-01 5.411 5.56e-07 ***
femininity 1.909e-01 3.084e-01 0.619 0.5376
damage 6.289e-05 2.570e-05 2.447 0.0165 *
femininity:damage -1.288e-06 2.551e-05 -0.050 0.9599
damage:post -9.840e-07 1.982e-05 -0.050 0.9605
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.136 on 86 degrees of freedom
Multiple R-squared: 0.3748, Adjusted R-squared: 0.3458
F-statistic: 12.89 on 4 and 86 DF, p-value: 2.89e-08fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2394 -0.8273 0.0007 0.6867 3.5661
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.00830 0.37293 -2.704 0.00823 **
femininity 0.04132 0.23246 0.178 0.85933
damage 0.40563 0.04558 8.899 6.61e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.026 on 88 degrees of freedom
Multiple R-squared: 0.4783, Adjusted R-squared: 0.4665
F-statistic: 40.34 on 2 and 88 DF, p-value: 3.681e-13fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3252 -0.7559 0.0173 0.6180 3.4805
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.9876455 0.3755394 -2.630 0.0101 *
femininity -0.0050906 0.2440909 -0.021 0.9834
damage 1.3609178 1.4822288 0.918 0.3611
damage:year -0.0004814 0.0007466 -0.645 0.5208
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.03 on 87 degrees of freedom
Multiple R-squared: 0.4808, Adjusted R-squared: 0.4629
F-statistic: 26.85 on 3 and 87 DF, p-value: 2.182e-12fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3177 -0.7507 0.0267 0.6101 3.4804
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.97007 0.37657 -2.576 0.0117 *
femininity -0.02251 0.24573 -0.092 0.9272
damage 0.41877 0.04843 8.647 2.37e-13 ***
damage:post -0.02377 0.02917 -0.815 0.4174
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.028 on 87 degrees of freedom
Multiple R-squared: 0.4823, Adjusted R-squared: 0.4644
F-statistic: 27.01 on 3 and 87 DF, p-value: 1.931e-12fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2507 -0.7400 0.0247 0.5989 3.5321
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.54721 0.55247 -0.990 0.325
femininity -0.72714 0.71874 -1.012 0.314
damage 0.34007 0.07375 4.611 1.37e-05 ***
femininity:damage 0.10588 0.09372 1.130 0.262
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.025 on 87 degrees of freedom
Multiple R-squared: 0.4859, Adjusted R-squared: 0.4681
F-statistic: 27.4 on 3 and 87 DF, p-value: 1.432e-12fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3144 -0.7361 -0.0140 0.5673 3.4701
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.5620429 0.5557986 -1.011 0.315
femininity -0.7114274 0.7226878 -0.984 0.328
damage 1.0619881 1.5092756 0.704 0.484
femininity:damage 0.0989079 0.0952561 1.038 0.302
damage:year -0.0003616 0.0007551 -0.479 0.633
Residual standard error: 1.029 on 86 degrees of freedom
Multiple R-squared: 0.4872, Adjusted R-squared: 0.4634
F-statistic: 20.43 on 4 and 86 DF, p-value: 7.385e-12fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3157 -0.7354 0.0182 0.5646 3.4626
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.54994 0.55420 -0.992 0.324
femininity -0.72269 0.72100 -1.002 0.319
damage 0.35608 0.07763 4.587 1.52e-05 ***
femininity:damage 0.09786 0.09475 1.033 0.305
damage:post -0.01999 0.02938 -0.681 0.498
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.028 on 86 degrees of freedom
Multiple R-squared: 0.4886, Adjusted R-squared: 0.4648
F-statistic: 20.54 on 4 and 86 DF, p-value: 6.591e-12fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.1918 -0.7830 -0.1413 0.8263 4.2045
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.184e+00 2.936e-01 4.031 0.000119 ***
femininity 3.334e-02 4.029e-02 0.828 0.410117
damage 7.544e-05 9.786e-06 7.709 1.92e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.087 on 87 degrees of freedom
Multiple R-squared: 0.409, Adjusted R-squared: 0.3954
F-statistic: 30.11 on 2 and 87 DF, p-value: 1.158e-10fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-1.8698 -0.7611 -0.1044 0.7778 4.1770
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.254e+00 3.004e-01 4.173 7.17e-05 ***
femininity 2.271e-02 4.143e-02 0.548 0.585
damage 8.753e-04 7.408e-04 1.182 0.241
damage:year -4.035e-07 3.736e-07 -1.080 0.283
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.086 on 86 degrees of freedom
Multiple R-squared: 0.4169, Adjusted R-squared: 0.3966
F-statistic: 20.5 on 3 and 86 DF, p-value: 4.13e-10fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7962 -0.7651 -0.0860 0.7456 4.1723
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.275e+00 2.987e-01 4.270 5.01e-05 ***
femininity 1.943e-02 4.118e-02 0.472 0.638
damage 8.809e-05 1.309e-05 6.731 1.79e-09 ***
damage:post -2.374e-05 1.645e-05 -1.444 0.152
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.081 on 86 degrees of freedom
Multiple R-squared: 0.423, Adjusted R-squared: 0.4029
F-statistic: 21.02 on 3 and 86 DF, p-value: 2.651e-10fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3595 -0.7214 -0.0572 0.7918 4.2290
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.347e+00 3.393e-01 3.969 0.000149 ***
femininity 7.454e-03 4.849e-02 0.154 0.878187
damage 5.489e-05 2.353e-05 2.332 0.022013 *
femininity:damage 3.245e-06 3.379e-06 0.960 0.339502
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.088 on 86 degrees of freedom
Multiple R-squared: 0.4153, Adjusted R-squared: 0.3949
F-statistic: 20.36 on 3 and 86 DF, p-value: 4.65e-10fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.0412 -0.7434 -0.0767 0.7863 4.1967
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.316e+00 3.442e-01 3.823 0.000251 ***
femininity 1.270e-02 4.940e-02 0.257 0.797755
damage 6.470e-04 9.600e-04 0.674 0.502151
femininity:damage 1.619e-06 4.295e-06 0.377 0.707251
damage:year -2.935e-07 4.756e-07 -0.617 0.538891
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.092 on 85 degrees of freedom
Multiple R-squared: 0.4179, Adjusted R-squared: 0.3905
F-statistic: 15.26 on 4 and 85 DF, p-value: 1.931e-09fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-1.8659 -0.7529 -0.0726 0.7524 4.1804
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.303e+00 3.414e-01 3.818 0.000255 ***
femininity 1.494e-02 4.893e-02 0.305 0.760881
damage 8.254e-05 3.476e-05 2.374 0.019835 *
femininity:damage 7.088e-07 4.113e-06 0.172 0.863582
damage:post -2.176e-05 2.015e-05 -1.080 0.283313
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.087 on 85 degrees of freedom
Multiple R-squared: 0.4232, Adjusted R-squared: 0.3961
F-statistic: 15.59 on 4 and 85 DF, p-value: 1.325e-09fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2435 -0.8396 -0.0093 0.7188 3.5550
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.05501 0.42238 -2.498 0.0144 *
femininity 0.01426 0.03827 0.373 0.7103
damage 0.40285 0.04624 8.712 1.74e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.031 on 87 degrees of freedom
Multiple R-squared: 0.4688, Adjusted R-squared: 0.4565
F-statistic: 38.38 on 2 and 87 DF, p-value: 1.122e-12fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3215 -0.7640 0.0171 0.6487 3.4802
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.0310891 0.4263990 -2.418 0.0177 *
femininity 0.0089466 0.0396615 0.226 0.8221
damage 1.2110229 1.4920562 0.812 0.4192
damage:year -0.0004071 0.0007513 -0.542 0.5893
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.035 on 86 degrees of freedom
Multiple R-squared: 0.4706, Adjusted R-squared: 0.4521
F-statistic: 25.48 on 3 and 86 DF, p-value: 6.894e-12fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3197 -0.7545 0.0313 0.6327 3.4765
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.01454 0.42739 -2.374 0.0198 *
femininity 0.00723 0.03963 0.182 0.8557
damage 0.41449 0.04917 8.429 7.14e-13 ***
damage:post -0.02068 0.02907 -0.711 0.4788
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.034 on 86 degrees of freedom
Multiple R-squared: 0.4719, Adjusted R-squared: 0.4534
F-statistic: 25.61 on 3 and 86 DF, p-value: 6.211e-12fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2533 -0.7657 0.0001 0.6455 3.5180
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.36510 0.83788 -0.436 0.66411
femininity -0.10059 0.12639 -0.796 0.42829
damage 0.30807 0.10963 2.810 0.00613 **
femininity:damage 0.01562 0.01638 0.954 0.34298
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.031 on 86 degrees of freedom
Multiple R-squared: 0.4743, Adjusted R-squared: 0.456
F-statistic: 25.87 on 3 and 86 DF, p-value: 5.098e-12fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3126 -0.7548 -0.0149 0.5877 3.4627
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.3892141 0.8439846 -0.461 0.646
femininity -0.0976042 0.1272096 -0.767 0.445
damage 0.9352789 1.5263766 0.613 0.542
femininity:damage 0.0146598 0.0166274 0.882 0.380
damage:year -0.0003130 0.0007598 -0.412 0.681
Residual standard error: 1.036 on 85 degrees of freedom
Multiple R-squared: 0.4754, Adjusted R-squared: 0.4507
F-statistic: 19.25 on 4 and 85 DF, p-value: 2.632e-11fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3181 -0.7412 -0.0093 0.5725 3.4531
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.38043 0.84135 -0.452 0.65230
femininity -0.09829 0.12691 -0.775 0.44078
damage 0.32497 0.11350 2.863 0.00528 **
femininity:damage 0.01449 0.01655 0.875 0.38383
damage:post -0.01778 0.02930 -0.607 0.54557
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.035 on 85 degrees of freedom
Multiple R-squared: 0.4766, Adjusted R-squared: 0.452
F-statistic: 19.35 on 4 and 85 DF, p-value: 2.39e-11fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.1944 -0.7802 -0.1214 0.7720 4.2063
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.251e+00 2.160e-01 5.791 1.09e-07 ***
femininity 2.179e-01 2.451e-01 0.889 0.376
damage 7.535e-05 9.782e-06 7.703 1.98e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.087 on 87 degrees of freedom
Multiple R-squared: 0.4097, Adjusted R-squared: 0.3962
F-statistic: 30.19 on 2 and 87 DF, p-value: 1.099e-10fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-1.8775 -0.7646 -0.0967 0.7326 4.1773
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.296e+00 2.199e-01 5.892 7.24e-08 ***
femininity 1.536e-01 2.523e-01 0.609 0.544
damage 8.637e-04 7.409e-04 1.166 0.247
damage:year -3.976e-07 3.736e-07 -1.064 0.290
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.086 on 86 degrees of freedom
Multiple R-squared: 0.4174, Adjusted R-squared: 0.3971
F-statistic: 20.54 on 3 and 86 DF, p-value: 3.991e-10fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-1.8031 -0.7576 -0.0851 0.7075 4.1729
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.312e+00 2.190e-01 5.992 4.71e-08 ***
femininity 1.308e-01 2.513e-01 0.520 0.604
damage 8.788e-05 1.312e-05 6.700 2.06e-09 ***
damage:post -2.347e-05 1.648e-05 -1.424 0.158
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.08 on 86 degrees of freedom
Multiple R-squared: 0.4233, Adjusted R-squared: 0.4032
F-statistic: 21.04 on 3 and 86 DF, p-value: 2.589e-10fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3850 -0.7221 -0.0500 0.7659 4.2335
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.357e+00 2.389e-01 5.678 1.81e-07 ***
femininity 4.902e-02 2.946e-01 0.166 0.868251
damage 6.193e-05 1.626e-05 3.809 0.000261 ***
femininity:damage 2.101e-05 2.035e-05 1.032 0.304751
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.086 on 86 degrees of freedom
Multiple R-squared: 0.4169, Adjusted R-squared: 0.3966
F-statistic: 20.5 on 3 and 86 DF, p-value: 4.122e-10fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.1013 -0.7339 -0.0690 0.7496 4.2036
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.344e+00 2.409e-01 5.578 2.83e-07 ***
femininity 7.420e-02 2.992e-01 0.248 0.805
damage 5.841e-04 9.315e-04 0.627 0.532
femininity:damage 1.263e-05 2.531e-05 0.499 0.619
damage:year -2.607e-07 4.649e-07 -0.561 0.577
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.091 on 85 degrees of freedom
Multiple R-squared: 0.4191, Adjusted R-squared: 0.3918
F-statistic: 15.33 on 4 and 85 DF, p-value: 1.773e-09fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-1.9220 -0.7556 -0.0681 0.7199 4.1867
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.339e+00 2.395e-01 5.593 2.66e-07 ***
femininity 8.547e-02 2.968e-01 0.288 0.77407
damage 8.159e-05 2.533e-05 3.221 0.00181 **
femininity:damage 7.135e-06 2.454e-05 0.291 0.77192
damage:post -2.022e-05 1.998e-05 -1.012 0.31438
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.086 on 85 degrees of freedom
Multiple R-squared: 0.4239, Adjusted R-squared: 0.3968
F-statistic: 15.64 on 4 and 85 DF, p-value: 1.261e-09fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2332 -0.8384 0.0048 0.6970 3.5737
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.99098 0.37860 -2.617 0.0104 *
femininity 0.03725 0.23399 0.159 0.8739
damage 0.40317 0.04643 8.682 2.01e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.032 on 87 degrees of freedom
Multiple R-squared: 0.4681, Adjusted R-squared: 0.4558
F-statistic: 38.28 on 2 and 87 DF, p-value: 1.188e-12fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3160 -0.7687 0.0218 0.6556 3.4907
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.9767899 0.3807562 -2.565 0.012 *
femininity -0.0052573 0.2454367 -0.021 0.983
damage 1.3043889 1.5119561 0.863 0.391
damage:year -0.0004538 0.0007610 -0.596 0.553
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.035 on 86 degrees of freedom
Multiple R-squared: 0.4703, Adjusted R-squared: 0.4518
F-statistic: 25.45 on 3 and 86 DF, p-value: 7.068e-12fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3107 -0.7654 0.0243 0.6404 3.4885
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.96043 0.38155 -2.517 0.0137 *
femininity -0.02267 0.24709 -0.092 0.9271
damage 0.41669 0.04974 8.377 9.11e-13 ***
damage:post -0.02286 0.02966 -0.771 0.4431
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.034 on 86 degrees of freedom
Multiple R-squared: 0.4717, Adjusted R-squared: 0.4533
F-statistic: 25.6 on 3 and 86 DF, p-value: 6.288e-12fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2469 -0.7412 -0.0011 0.6124 3.5371
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.54721 0.55557 -0.985 0.327
femininity -0.71321 0.72684 -0.981 0.329
damage 0.34007 0.07416 4.585 1.53e-05 ***
femininity:damage 0.10364 0.09505 1.090 0.279
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.03 on 86 degrees of freedom
Multiple R-squared: 0.4753, Adjusted R-squared: 0.457
F-statistic: 25.97 on 3 and 86 DF, p-value: 4.701e-12fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3098 -0.7383 -0.0218 0.5807 3.4754
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.5615261 0.5590363 -1.004 0.318
femininity -0.7032352 0.7305429 -0.963 0.338
damage 1.0368295 1.5345414 0.676 0.501
femininity:damage 0.0977489 0.0963648 1.014 0.313
damage:year -0.0003490 0.0007678 -0.455 0.651
Residual standard error: 1.035 on 85 degrees of freedom
Multiple R-squared: 0.4766, Adjusted R-squared: 0.452
F-statistic: 19.35 on 4 and 85 DF, p-value: 2.388e-11fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3126 -0.7395 0.0087 0.5766 3.4664
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.54989 0.55742 -0.986 0.327
femininity -0.71577 0.72926 -0.982 0.329
damage 0.35579 0.07815 4.553 1.75e-05 ***
femininity:damage 0.09689 0.09591 1.010 0.315
damage:post -0.01963 0.02983 -0.658 0.512
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.034 on 85 degrees of freedom
Multiple R-squared: 0.478, Adjusted R-squared: 0.4534
F-statistic: 19.46 on 4 and 85 DF, p-value: 2.139e-11fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.8729 -0.7717 -0.1632 0.8794 2.5164
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.331e+00 2.788e-01 4.776 7.00e-06 ***
femininity 2.658e-02 3.853e-02 0.690 0.492
damage 5.138e-05 6.255e-06 8.215 1.57e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.057 on 89 degrees of freedom
Multiple R-squared: 0.4325, Adjusted R-squared: 0.4197
F-statistic: 33.91 on 2 and 89 DF, p-value: 1.128e-11fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.0070 -0.7759 -0.1742 0.8569 2.4455
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.363e+00 2.833e-01 4.812 6.15e-06 ***
femininity 2.105e-02 3.944e-02 0.534 0.595
damage 4.057e-04 5.085e-04 0.798 0.427
damage:year -1.784e-07 2.560e-07 -0.697 0.488
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.061 on 88 degrees of freedom
Multiple R-squared: 0.4356, Adjusted R-squared: 0.4164
F-statistic: 22.64 on 3 and 88 DF, p-value: 5.937e-11fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3523 -0.7691 -0.1368 0.8716 2.2887
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.404e+00 2.834e-01 4.955 3.47e-06 ***
femininity 1.379e-02 3.964e-02 0.348 0.729
damage 6.079e-05 9.579e-06 6.346 9.32e-09 ***
damage:post -1.473e-05 1.139e-05 -1.293 0.199
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.054 on 88 degrees of freedom
Multiple R-squared: 0.4431, Adjusted R-squared: 0.4241
F-statistic: 23.33 on 3 and 88 DF, p-value: 3.332e-11fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.1499 -0.8088 -0.1218 0.8328 2.3868
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.502e+00 3.087e-01 4.868 4.93e-06 ***
femininity -1.591e-03 4.432e-02 -0.036 0.97145
damage 3.615e-05 1.350e-05 2.677 0.00886 **
femininity:damage 2.541e-06 1.997e-06 1.272 0.20666
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.054 on 88 degrees of freedom
Multiple R-squared: 0.4427, Adjusted R-squared: 0.4237
F-statistic: 23.3 on 3 and 88 DF, p-value: 3.418e-11fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2408 -0.8021 -0.1208 0.8298 2.3706
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.501e+00 3.104e-01 4.836 5.67e-06 ***
femininity -1.466e-03 4.456e-02 -0.033 0.974
damage 1.674e-04 5.537e-04 0.302 0.763
femininity:damage 2.346e-06 2.168e-06 1.082 0.282
damage:year -6.548e-08 2.762e-07 -0.237 0.813
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.06 on 87 degrees of freedom
Multiple R-squared: 0.4431, Adjusted R-squared: 0.4175
F-statistic: 17.3 on 4 and 87 DF, p-value: 1.773e-10fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.4157 -0.7980 -0.1140 0.8686 2.2877
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.485e+00 3.106e-01 4.782 7.02e-06 ***
femininity 7.269e-04 4.458e-02 0.016 0.9870
damage 4.800e-05 2.194e-05 2.188 0.0314 *
femininity:damage 1.582e-06 2.441e-06 0.648 0.5185
damage:post -9.565e-06 1.393e-05 -0.687 0.4941
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.057 on 87 degrees of freedom
Multiple R-squared: 0.4457, Adjusted R-squared: 0.4202
F-statistic: 17.49 on 4 and 87 DF, p-value: 1.449e-10fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.21829 -0.79120 0.01354 0.69150 2.35338
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.084216 0.386446 -2.806 0.00617 **
femininity 0.003939 0.035069 0.112 0.91082
damage 0.413804 0.041311 10.017 2.96e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9614 on 89 degrees of freedom
Multiple R-squared: 0.5309, Adjusted R-squared: 0.5204
F-statistic: 50.37 on 2 and 89 DF, p-value: 2.346e-15fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.18511 -0.80469 -0.00067 0.70300 2.37292
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.0926897 0.3899778 -2.802 0.00625 **
femininity 0.0061170 0.0363191 0.168 0.86664
damage 0.0779617 1.3460277 0.058 0.95394
damage:year 0.0001691 0.0006773 0.250 0.80346
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9665 on 88 degrees of freedom
Multiple R-squared: 0.5313, Adjusted R-squared: 0.5153
F-statistic: 33.25 on 3 and 88 DF, p-value: 1.844e-14fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.23009 -0.78184 0.02572 0.67971 2.33814
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.078300 0.391818 -2.752 0.00719 **
femininity 0.002813 0.036532 0.077 0.93881
damage 0.415688 0.044498 9.342 8.12e-15 ***
damage:post -0.003141 0.026586 -0.118 0.90623
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9668 on 88 degrees of freedom
Multiple R-squared: 0.531, Adjusted R-squared: 0.515
F-statistic: 33.21 on 3 and 88 DF, p-value: 1.889e-14fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.22381 -0.73966 0.06966 0.67468 2.25590
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.49352 0.75785 -0.651 0.51661
femininity -0.09454 0.11418 -0.828 0.40991
damage 0.33445 0.09682 3.454 0.00085 ***
femininity:damage 0.01312 0.01448 0.906 0.36720
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9624 on 88 degrees of freedom
Multiple R-squared: 0.5353, Adjusted R-squared: 0.5194
F-statistic: 33.78 on 3 and 88 DF, p-value: 1.269e-14fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.17427 -0.75689 0.07149 0.71270 2.27992
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.4739390 0.7634129 -0.621 0.536
femininity -0.0966551 0.1148826 -0.841 0.402
damage -0.1743466 1.3731931 -0.127 0.899
femininity:damage 0.0138411 0.0146776 0.943 0.348
damage:year 0.0002539 0.0006836 0.371 0.711
Residual standard error: 0.9671 on 87 degrees of freedom
Multiple R-squared: 0.536, Adjusted R-squared: 0.5147
F-statistic: 25.12 on 4 and 87 DF, p-value: 7.576e-14fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.22357 -0.73963 0.06966 0.67494 2.25618
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.934e-01 7.631e-01 -0.647 0.5196
femininity -9.455e-02 1.149e-01 -0.823 0.4130
damage 3.344e-01 1.013e-01 3.301 0.0014 **
femininity:damage 1.313e-02 1.469e-02 0.893 0.3741
damage:post 6.359e-05 2.686e-02 0.002 0.9981
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9679 on 87 degrees of freedom
Multiple R-squared: 0.5353, Adjusted R-squared: 0.5139
F-statistic: 25.05 on 4 and 87 DF, p-value: 8.106e-14fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.8642 -0.7372 -0.1798 0.8560 2.5227
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.376e+00 2.040e-01 6.745 1.49e-09 ***
femininity 1.869e-01 2.350e-01 0.795 0.428
damage 5.132e-05 6.249e-06 8.213 1.59e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.057 on 89 degrees of freedom
Multiple R-squared: 0.4335, Adjusted R-squared: 0.4207
F-statistic: 34.05 on 2 and 89 DF, p-value: 1.043e-11fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-1.9953 -0.7297 -0.1696 0.8436 2.4493
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.393e+00 2.061e-01 6.758 1.46e-09 ***
femininity 1.564e-01 2.398e-01 0.652 0.516
damage 3.990e-04 5.065e-04 0.788 0.433
damage:year -1.750e-07 2.550e-07 -0.686 0.494
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.06 on 88 degrees of freedom
Multiple R-squared: 0.4365, Adjusted R-squared: 0.4173
F-statistic: 22.72 on 3 and 88 DF, p-value: 5.542e-11fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3386 -0.7325 -0.1497 0.8723 2.2914
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.416e+00 2.057e-01 6.883 8.26e-10 ***
femininity 1.140e-01 2.411e-01 0.473 0.637
damage 6.057e-05 9.575e-06 6.326 1.02e-08 ***
damage:post -1.444e-05 1.135e-05 -1.272 0.207
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.053 on 88 degrees of freedom
Multiple R-squared: 0.4437, Adjusted R-squared: 0.4247
F-statistic: 23.4 on 3 and 88 DF, p-value: 3.168e-11fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.0557 -0.7964 -0.1252 0.8411 2.4148
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.478e+00 2.199e-01 6.722 1.72e-09 ***
femininity 2.010e-02 2.710e-01 0.074 0.941
damage 4.169e-05 1.004e-05 4.154 7.54e-05 ***
femininity:damage 1.568e-05 1.280e-05 1.225 0.224
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.054 on 88 degrees of freedom
Multiple R-squared: 0.443, Adjusted R-squared: 0.424
F-statistic: 23.33 on 3 and 88 DF, p-value: 3.356e-11fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.1900 -0.7946 -0.1197 0.8354 2.3883
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.478e+00 2.211e-01 6.684 2.12e-09 ***
femininity 1.974e-02 2.724e-01 0.072 0.942
damage 2.121e-04 5.363e-04 0.396 0.693
femininity:damage 1.431e-05 1.356e-05 1.055 0.294
damage:year -8.537e-08 2.686e-07 -0.318 0.751
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.059 on 87 degrees of freedom
Multiple R-squared: 0.4436, Adjusted R-squared: 0.418
F-statistic: 17.34 on 4 and 87 DF, p-value: 1.704e-10fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3809 -0.7808 -0.1202 0.8411 2.2955
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.468e+00 2.209e-01 6.648 2.5e-09 ***
femininity 3.112e-02 2.721e-01 0.114 0.90923
damage 5.156e-05 1.661e-05 3.103 0.00258 **
femininity:damage 9.940e-06 1.496e-05 0.665 0.50808
damage:post -9.911e-06 1.327e-05 -0.747 0.45711
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.056 on 87 degrees of freedom
Multiple R-squared: 0.4465, Adjusted R-squared: 0.4211
F-statistic: 17.55 on 4 and 87 DF, p-value: 1.366e-10fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.20749 -0.77632 0.00704 0.70042 2.36645
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.05316 0.34544 -3.049 0.00303 **
femininity -0.01431 0.21479 -0.067 0.94702
damage 0.41431 0.04144 9.997 3.25e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9614 on 89 degrees of freedom
Multiple R-squared: 0.5309, Adjusted R-squared: 0.5203
F-statistic: 50.36 on 2 and 89 DF, p-value: 2.355e-15fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.18115 -0.79137 0.00837 0.70889 2.38334
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.0562941 0.3476478 -3.038 0.00313 **
femininity -0.0015946 0.2246814 -0.007 0.99435
damage 0.1354385 1.3602044 0.100 0.92091
damage:year 0.0001403 0.0006841 0.205 0.83796
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9666 on 88 degrees of freedom
Multiple R-squared: 0.5311, Adjusted R-squared: 0.5151
F-statistic: 33.23 on 3 and 88 DF, p-value: 1.87e-14fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2238 -0.7624 0.0227 0.6843 2.3432
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.047560 0.348837 -3.003 0.00348 **
femininity -0.026898 0.227848 -0.118 0.90629
damage 0.417249 0.044997 9.273 1.13e-14 ***
damage:post -0.004693 0.027075 -0.173 0.86277
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9667 on 88 degrees of freedom
Multiple R-squared: 0.531, Adjusted R-squared: 0.5151
F-statistic: 33.22 on 3 and 88 DF, p-value: 1.882e-14fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.21631 -0.71866 0.08346 0.65840 2.26913
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.63652 0.50514 -1.260 0.211
femininity -0.71937 0.66033 -1.089 0.279
damage 0.35627 0.06600 5.398 5.63e-07 ***
femininity:damage 0.09564 0.08472 1.129 0.262
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.96 on 88 degrees of freedom
Multiple R-squared: 0.5376, Adjusted R-squared: 0.5218
F-statistic: 34.1 on 3 and 88 DF, p-value: 1.021e-14fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.17313 -0.74898 0.08838 0.64232 2.29363
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.6272109 0.5084550 -1.234 0.221
femininity -0.7228708 0.6637622 -1.089 0.279
damage -0.1060436 1.3736555 -0.077 0.939
femininity:damage 0.0989635 0.0857169 1.155 0.251
damage:year 0.0002316 0.0006874 0.337 0.737
Residual standard error: 0.9648 on 87 degrees of freedom
Multiple R-squared: 0.5382, Adjusted R-squared: 0.5169
F-statistic: 25.35 on 4 and 87 DF, p-value: 6.196e-14fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.22037 -0.71531 0.08359 0.65421 2.26372
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.636992 0.508147 -1.254 0.213
femininity -0.719349 0.664107 -1.083 0.282
damage 0.357271 0.070284 5.083 2.1e-06 ***
femininity:damage 0.095208 0.085784 1.110 0.270
damage:post -0.001182 0.027224 -0.043 0.965
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9654 on 87 degrees of freedom
Multiple R-squared: 0.5376, Adjusted R-squared: 0.5163
F-statistic: 25.29 on 4 and 87 DF, p-value: 6.544e-14fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.0114 -0.7856 -0.1292 0.8934 2.4737
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.310e+00 2.819e-01 4.648 1.17e-05 ***
femininity 2.819e-02 3.875e-02 0.727 0.469
damage 5.312e-05 6.897e-06 7.702 1.88e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.061 on 88 degrees of freedom
Multiple R-squared: 0.4033, Adjusted R-squared: 0.3898
F-statistic: 29.74 on 2 and 88 DF, p-value: 1.355e-10fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-1.9842 -0.7903 -0.1737 0.8577 2.4415
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.344e+00 2.942e-01 4.568 1.61e-05 ***
femininity 2.329e-02 4.060e-02 0.574 0.568
damage 3.162e-04 6.187e-04 0.511 0.611
damage:year -1.329e-07 3.125e-07 -0.425 0.672
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.066 on 87 degrees of freedom
Multiple R-squared: 0.4046, Adjusted R-squared: 0.384
F-statistic: 19.7 on 3 and 87 DF, p-value: 7.778e-10fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3542 -0.7788 -0.1463 0.8840 2.2879
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.396e+00 2.912e-01 4.793 6.72e-06 ***
femininity 1.477e-02 4.044e-02 0.365 0.716
damage 6.081e-05 9.635e-06 6.312 1.12e-08 ***
damage:post -1.407e-05 1.233e-05 -1.141 0.257
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.059 on 87 degrees of freedom
Multiple R-squared: 0.4121, Adjusted R-squared: 0.3919
F-statistic: 20.33 on 3 and 87 DF, p-value: 4.498e-10fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.4883 -0.7914 -0.1100 0.8548 2.2826
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.506e+00 3.086e-01 4.880 4.76e-06 ***
femininity -5.411e-03 4.447e-02 -0.122 0.903
damage 3.557e-05 1.351e-05 2.633 0.010 *
femininity:damage 3.136e-06 2.081e-06 1.507 0.136
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.054 on 87 degrees of freedom
Multiple R-squared: 0.4185, Adjusted R-squared: 0.3985
F-statistic: 20.87 on 3 and 87 DF, p-value: 2.822e-10fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.32887 -0.78564 -0.07467 0.85591 2.26969
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.517e+00 3.095e-01 4.901 4.45e-06 ***
femininity -9.638e-03 4.487e-02 -0.215 0.830
damage -6.521e-04 8.474e-04 -0.770 0.444
femininity:damage 4.710e-06 2.848e-06 1.654 0.102
damage:year 3.429e-07 4.225e-07 0.812 0.419
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.056 on 86 degrees of freedom
Multiple R-squared: 0.4229, Adjusted R-squared: 0.3961
F-statistic: 15.76 on 4 and 86 DF, p-value: 1.037e-09fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.4901 -0.7915 -0.1102 0.8550 2.2818
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.506e+00 3.127e-01 4.816 6.22e-06 ***
femininity -5.346e-03 4.545e-02 -0.118 0.907
damage 3.577e-05 2.755e-05 1.298 0.198
femininity:damage 3.116e-06 3.211e-06 0.970 0.335
damage:post -1.531e-07 1.892e-05 -0.008 0.994
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.06 on 86 degrees of freedom
Multiple R-squared: 0.4185, Adjusted R-squared: 0.3915
F-statistic: 15.47 on 4 and 86 DF, p-value: 1.426e-09fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.1934 -0.7861 0.0246 0.6921 2.4050
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.005704 0.386686 -2.601 0.0109 *
femininity 0.001288 0.034833 0.037 0.9706
damage 0.403412 0.041524 9.715 1.38e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9538 on 88 degrees of freedom
Multiple R-squared: 0.5181, Adjusted R-squared: 0.5071
F-statistic: 47.3 on 2 and 88 DF, p-value: 1.127e-14fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.21042 -0.77844 0.03379 0.67428 2.39578
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -9.996e-01 3.917e-01 -2.552 0.0125 *
femininity 8.563e-05 3.626e-02 0.002 0.9981
damage 5.801e-01 1.375e+00 0.422 0.6742
damage:year -8.907e-05 6.929e-04 -0.129 0.8980
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9592 on 87 degrees of freedom
Multiple R-squared: 0.5181, Adjusted R-squared: 0.5015
F-statistic: 31.18 on 3 and 87 DF, p-value: 8.786e-14fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.22880 -0.76279 0.04973 0.65440 2.36003
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.984669 0.392934 -2.506 0.0141 *
femininity -0.002270 0.036362 -0.062 0.9504
damage 0.408825 0.044330 9.222 1.57e-14 ***
damage:post -0.009653 0.026677 -0.362 0.7184
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9585 on 87 degrees of freedom
Multiple R-squared: 0.5188, Adjusted R-squared: 0.5022
F-statistic: 31.26 on 3 and 87 DF, p-value: 8.303e-14fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.19930 -0.75676 0.02321 0.68723 2.32134
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.51865 0.75314 -0.689 0.492878
femininity -0.08044 0.11385 -0.707 0.481697
damage 0.33798 0.09622 3.512 0.000706 ***
femininity:damage 0.01091 0.01446 0.754 0.452716
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9561 on 87 degrees of freedom
Multiple R-squared: 0.5212, Adjusted R-squared: 0.5047
F-statistic: 31.57 on 3 and 87 DF, p-value: 6.691e-14fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.2001 -0.7564 0.0227 0.6864 2.3210
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.190e-01 7.598e-01 -0.683 0.496
femininity -8.039e-02 1.148e-01 -0.700 0.486
damage 3.468e-01 1.415e+00 0.245 0.807
femininity:damage 1.089e-02 1.474e-02 0.739 0.462
damage:year -4.399e-06 7.041e-04 -0.006 0.995
Residual standard error: 0.9617 on 86 degrees of freedom
Multiple R-squared: 0.5212, Adjusted R-squared: 0.4989
F-statistic: 23.4 on 4 and 86 DF, p-value: 4.135e-13fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.22374 -0.74583 0.01635 0.66108 2.29415
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.528875 0.758340 -0.697 0.487427
femininity -0.078748 0.114666 -0.687 0.494081
damage 0.345115 0.100887 3.421 0.000957 ***
femininity:damage 0.010351 0.014714 0.703 0.483652
damage:post -0.006754 0.027071 -0.249 0.803570
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9613 on 86 degrees of freedom
Multiple R-squared: 0.5215, Adjusted R-squared: 0.4993
F-statistic: 23.44 on 4 and 86 DF, p-value: 4.011e-13fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.0060 -0.7685 -0.1697 0.8633 2.4791
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.357e+00 2.069e-01 6.558 3.61e-09 ***
femininity 1.987e-01 2.365e-01 0.840 0.403
damage 5.310e-05 6.888e-06 7.709 1.81e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.06 on 88 degrees of freedom
Multiple R-squared: 0.4045, Adjusted R-squared: 0.391
F-statistic: 29.89 on 2 and 88 DF, p-value: 1.242e-10fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-1.9690 -0.7688 -0.1695 0.8524 2.4461
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.378e+00 2.141e-01 6.433 6.55e-09 ***
femininity 1.717e-01 2.470e-01 0.695 0.489
damage 3.003e-04 6.161e-04 0.488 0.627
damage:year -1.249e-07 3.111e-07 -0.401 0.689
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.065 on 87 degrees of freedom
Multiple R-squared: 0.4056, Adjusted R-squared: 0.3851
F-statistic: 19.79 on 3 and 87 DF, p-value: 7.213e-10fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3403 -0.7400 -0.1496 0.8731 2.2911
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.408e+00 2.118e-01 6.651 2.46e-09 ***
femininity 1.213e-01 2.462e-01 0.493 0.624
damage 6.060e-05 9.629e-06 6.293 1.22e-08 ***
damage:post -1.367e-05 1.229e-05 -1.113 0.269
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.059 on 87 degrees of freedom
Multiple R-squared: 0.4129, Adjusted R-squared: 0.3926
F-statistic: 20.39 on 3 and 87 DF, p-value: 4.263e-10fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.4619 -0.7807 -0.1007 0.8503 2.2905
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.478e+00 2.195e-01 6.734 1.7e-09 ***
femininity -1.723e-02 2.725e-01 -0.063 0.950
damage 4.169e-05 1.002e-05 4.162 7.4e-05 ***
femininity:damage 2.134e-05 1.370e-05 1.558 0.123
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.052 on 87 degrees of freedom
Multiple R-squared: 0.4207, Adjusted R-squared: 0.4007
F-statistic: 21.06 on 3 and 87 DF, p-value: 2.402e-10fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.25835 -0.78607 -0.06969 0.87559 2.28948
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.481e+00 2.198e-01 6.736 1.75e-09 ***
femininity -5.017e-02 2.754e-01 -0.182 0.8559
damage -6.931e-04 8.345e-04 -0.831 0.4085
femininity:damage 3.246e-05 1.864e-05 1.741 0.0852 .
damage:year 3.681e-07 4.180e-07 0.881 0.3810
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.053 on 86 degrees of freedom
Multiple R-squared: 0.4259, Adjusted R-squared: 0.3992
F-statistic: 15.95 on 4 and 86 DF, p-value: 8.383e-10fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.4449 -0.7803 -0.1016 0.8514 2.2980
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.479e+00 2.215e-01 6.678 2.28e-09 ***
femininity -2.058e-02 2.790e-01 -0.074 0.9414
damage 4.050e-05 2.097e-05 1.932 0.0567 .
femininity:damage 2.234e-05 2.071e-05 1.079 0.2837
damage:post 1.189e-06 1.845e-05 0.064 0.9488
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.058 on 86 degrees of freedom
Multiple R-squared: 0.4207, Adjusted R-squared: 0.3938
F-statistic: 15.61 on 4 and 86 DF, p-value: 1.217e-09fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.18046 -0.77122 -0.00139 0.70115 2.41765
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.97943 0.34585 -2.832 0.00573 **
femininity -0.03389 0.21342 -0.159 0.87420
damage 0.40404 0.04163 9.706 1.44e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9536 on 88 degrees of freedom
Multiple R-squared: 0.5182, Adjusted R-squared: 0.5072
F-statistic: 47.32 on 2 and 88 DF, p-value: 1.113e-14fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.20451 -0.75950 0.00986 0.68001 2.40329
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.9741636 0.3488771 -2.792 0.00643 **
femininity -0.0465182 0.2247436 -0.207 0.83651
damage 0.6672278 1.3918714 0.479 0.63288
damage:year -0.0001326 0.0007009 -0.189 0.85039
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9589 on 87 degrees of freedom
Multiple R-squared: 0.5184, Adjusted R-squared: 0.5018
F-statistic: 31.21 on 3 and 87 DF, p-value: 8.602e-14fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.22026 -0.74156 0.02246 0.66413 2.36151
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.96191 0.34978 -2.750 0.00725 **
femininity -0.06652 0.22715 -0.293 0.77035
damage 0.41098 0.04476 9.181 1.91e-14 ***
damage:post -0.01183 0.02720 -0.435 0.66460
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9581 on 87 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5027
F-statistic: 31.32 on 3 and 87 DF, p-value: 7.974e-14fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.18995 -0.70468 0.05119 0.66001 2.33223
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.63652 0.50214 -1.268 0.208
femininity -0.62236 0.65989 -0.943 0.348
damage 0.35627 0.06560 5.431 5.03e-07 ***
femininity:damage 0.08003 0.08492 0.942 0.349
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9543 on 87 degrees of freedom
Multiple R-squared: 0.5231, Adjusted R-squared: 0.5066
F-statistic: 31.8 on 3 and 87 DF, p-value: 5.654e-14fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.19580 -0.70273 0.04766 0.65771 2.32934
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -6.378e-01 5.059e-01 -1.261 0.211
femininity -6.210e-01 6.644e-01 -0.935 0.353
damage 4.214e-01 1.419e+00 0.297 0.767
femininity:damage 7.942e-02 8.643e-02 0.919 0.361
damage:year -3.265e-05 7.099e-04 -0.046 0.963
Residual standard error: 0.9598 on 86 degrees of freedom
Multiple R-squared: 0.5231, Adjusted R-squared: 0.5009
F-statistic: 23.58 on 4 and 86 DF, p-value: 3.503e-13fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.21783 -0.70434 0.03556 0.64832 2.29623
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.639899 0.504899 -1.267 0.208
femininity -0.618376 0.663482 -0.932 0.354
damage 0.363417 0.069962 5.195 1.36e-06 ***
femininity:damage 0.076333 0.086213 0.885 0.378
damage:post -0.008419 0.027501 -0.306 0.760
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9593 on 86 degrees of freedom
Multiple R-squared: 0.5236, Adjusted R-squared: 0.5014
F-statistic: 23.63 on 4 and 86 DF, p-value: 3.35e-13fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.44147 -0.70756 -0.09223 0.80975 2.30000
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.345e+00 2.755e-01 4.882 4.73e-06 ***
femininity 1.273e-02 3.839e-02 0.332 0.741
damage 6.281e-05 7.912e-06 7.939 6.58e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.036 on 87 degrees of freedom
Multiple R-squared: 0.4225, Adjusted R-squared: 0.4092
F-statistic: 31.82 on 2 and 87 DF, p-value: 4.256e-11fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.32259 -0.73668 -0.09668 0.82313 2.31630
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.321e+00 2.874e-01 4.595 1.47e-05 ***
femininity 1.570e-02 3.978e-02 0.395 0.694
damage -1.326e-04 6.349e-04 -0.209 0.835
damage:year 9.890e-08 3.213e-07 0.308 0.759
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.041 on 86 degrees of freedom
Multiple R-squared: 0.4231, Adjusted R-squared: 0.403
F-statistic: 21.02 on 3 and 86 DF, p-value: 2.633e-10fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.43216 -0.70990 -0.09256 0.81013 2.30328
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.343e+00 2.875e-01 4.671 1.10e-05 ***
femininity 1.295e-02 3.976e-02 0.326 0.745
damage 6.269e-05 9.518e-06 6.587 3.42e-09 ***
damage:post 3.267e-07 1.409e-05 0.023 0.982
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.042 on 86 degrees of freedom
Multiple R-squared: 0.4225, Adjusted R-squared: 0.4023
F-statistic: 20.97 on 3 and 86 DF, p-value: 2.758e-10fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3747 -0.7024 -0.1026 0.8234 2.3185
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.314e+00 3.241e-01 4.056 0.00011 ***
femininity 1.719e-02 4.580e-02 0.375 0.70837
damage 6.655e-05 2.214e-05 3.005 0.00348 **
femininity:damage -5.301e-07 2.933e-06 -0.181 0.85699
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.041 on 86 degrees of freedom
Multiple R-squared: 0.4227, Adjusted R-squared: 0.4025
F-statistic: 20.99 on 3 and 86 DF, p-value: 2.715e-10fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.3286 -0.7361 -0.0939 0.8221 2.3125
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.328e+00 3.301e-01 4.021 0.000125 ***
femininity 1.466e-02 4.714e-02 0.311 0.756538
damage -1.589e-04 8.979e-04 -0.177 0.859993
femininity:damage 1.688e-07 4.055e-06 0.042 0.966892
damage:year 1.116e-07 4.444e-07 0.251 0.802338
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.047 on 85 degrees of freedom
Multiple R-squared: 0.4231, Adjusted R-squared: 0.396
F-statistic: 15.58 on 4 and 85 DF, p-value: 1.334e-09fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.4012 -0.7154 -0.1066 0.8262 2.3062
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.309e+00 3.290e-01 3.979 0.000145 ***
femininity 1.830e-02 4.691e-02 0.390 0.697504
damage 6.966e-05 3.343e-05 2.084 0.040171 *
femininity:damage -8.493e-07 3.901e-06 -0.218 0.828181
damage:post -2.343e-06 1.874e-05 -0.125 0.900800
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.047 on 85 degrees of freedom
Multiple R-squared: 0.4228, Adjusted R-squared: 0.3956
F-statistic: 15.56 on 4 and 85 DF, p-value: 1.365e-09fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.19112 -0.80430 0.03142 0.69795 2.41478
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.001973 0.388181 -2.581 0.0115 *
femininity 0.005032 0.035536 0.142 0.8877
damage 0.398856 0.042391 9.409 6.53e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9573 on 87 degrees of freedom
Multiple R-squared: 0.5064, Adjusted R-squared: 0.495
F-statistic: 44.62 on 2 and 87 DF, p-value: 4.603e-14fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.21075 -0.78479 0.04251 0.67656 2.40419
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.9948799 0.3933026 -2.530 0.0132 *
femininity 0.0036717 0.0368970 0.100 0.9210
damage 0.6034857 1.3810964 0.437 0.6632
damage:year -0.0001032 0.0006959 -0.148 0.8825
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9628 on 86 degrees of freedom
Multiple R-squared: 0.5065, Adjusted R-squared: 0.4893
F-statistic: 29.42 on 3 and 86 DF, p-value: 3.48e-13fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.23251 -0.75751 0.05769 0.65333 2.36262
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.976968 0.394507 -2.476 0.0152 *
femininity 0.001121 0.036890 0.030 0.9758
damage 0.404888 0.044929 9.012 4.66e-14 ***
damage:post -0.011350 0.026908 -0.422 0.6742
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9619 on 86 degrees of freedom
Multiple R-squared: 0.5074, Adjusted R-squared: 0.4902
F-statistic: 29.53 on 3 and 86 DF, p-value: 3.222e-13fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.19768 -0.73660 0.01623 0.65071 2.31058
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.36880 0.77660 -0.475 0.63607
femininity -0.09931 0.11638 -0.853 0.39587
damage 0.31201 0.10152 3.073 0.00283 **
femininity:damage 0.01415 0.01502 0.942 0.34906
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.958 on 86 degrees of freedom
Multiple R-squared: 0.5114, Adjusted R-squared: 0.4944
F-statistic: 30 on 3 and 86 DF, p-value: 2.274e-13fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.19753 -0.73663 0.01632 0.65083 2.31064
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.687e-01 7.835e-01 -0.471 0.639
femininity -9.932e-02 1.174e-01 -0.846 0.400
damage 3.104e-01 1.418e+00 0.219 0.827
femininity:damage 1.415e-02 1.531e-02 0.924 0.358
damage:year 8.204e-07 7.055e-04 0.001 0.999
Residual standard error: 0.9636 on 85 degrees of freedom
Multiple R-squared: 0.5114, Adjusted R-squared: 0.4884
F-statistic: 22.24 on 4 and 85 DF, p-value: 1.371e-12fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.22726 -0.72395 0.01475 0.63232 2.27738
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.377652 0.781291 -0.483 0.6301
femininity -0.097698 0.117124 -0.834 0.4065
damage 0.320054 0.105490 3.034 0.0032 **
femininity:damage 0.013545 0.015235 0.889 0.3765
damage:post -0.008187 0.027175 -0.301 0.7639
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9631 on 85 degrees of freedom
Multiple R-squared: 0.5119, Adjusted R-squared: 0.489
F-statistic: 22.29 on 4 and 85 DF, p-value: 1.312e-12fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.44225 -0.72109 -0.09184 0.83137 2.29590
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.350e+00 2.020e-01 6.684 2.12e-09 ***
femininity 1.135e-01 2.338e-01 0.486 0.628
damage 6.274e-05 7.906e-06 7.936 6.66e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.035 on 87 degrees of freedom
Multiple R-squared: 0.4233, Adjusted R-squared: 0.41
F-statistic: 31.93 on 2 and 87 DF, p-value: 3.997e-11fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.30882 -0.75421 -0.09351 0.84762 2.31573
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.332e+00 2.100e-01 6.341 1.02e-08 ***
femininity 1.330e-01 2.417e-01 0.550 0.584
damage -1.538e-04 6.332e-04 -0.243 0.809
damage:year 1.096e-07 3.205e-07 0.342 0.733
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.04 on 86 degrees of freedom
Multiple R-squared: 0.4241, Adjusted R-squared: 0.404
F-statistic: 21.11 on 3 and 86 DF, p-value: 2.45e-10fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.41779 -0.72513 -0.09213 0.83375 2.30471
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.347e+00 2.104e-01 6.401 7.81e-09 ***
femininity 1.170e-01 2.421e-01 0.483 0.630
damage 6.243e-05 9.509e-06 6.565 3.77e-09 ***
damage:post 8.492e-07 1.408e-05 0.060 0.952
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.041 on 86 degrees of freedom
Multiple R-squared: 0.4233, Adjusted R-squared: 0.4032
F-statistic: 21.04 on 3 and 86 DF, p-value: 2.59e-10fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.46187 -0.72457 -0.08919 0.82753 2.29048
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.357e+00 2.289e-01 5.926 6.26e-08 ***
femininity 1.044e-01 2.792e-01 0.374 0.709279
damage 6.193e-05 1.558e-05 3.976 0.000146 ***
femininity:damage 1.096e-06 1.811e-05 0.060 0.951906
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.041 on 86 degrees of freedom
Multiple R-squared: 0.4233, Adjusted R-squared: 0.4032
F-statistic: 21.04 on 3 and 86 DF, p-value: 2.59e-10fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.34291 -0.75363 -0.07312 0.83506 2.28990
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.367e+00 2.308e-01 5.922 6.56e-08 ***
femininity 7.625e-02 2.859e-01 0.267 0.790
damage -3.690e-04 8.557e-04 -0.431 0.667
femininity:damage 9.078e-06 2.413e-05 0.376 0.708
damage:year 2.152e-07 4.271e-07 0.504 0.616
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.045 on 85 degrees of freedom
Multiple R-squared: 0.425, Adjusted R-squared: 0.398
F-statistic: 15.71 on 4 and 85 DF, p-value: 1.162e-09fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.42897 -0.74152 -0.08542 0.82527 2.30515
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.359e+00 2.308e-01 5.887 7.61e-08 ***
femininity 9.824e-02 2.850e-01 0.345 0.7311
damage 5.969e-05 2.369e-05 2.520 0.0136 *
femininity:damage 2.977e-06 2.352e-05 0.127 0.8996
damage:post 2.312e-06 1.827e-05 0.127 0.8996
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.047 on 85 degrees of freedom
Multiple R-squared: 0.4234, Adjusted R-squared: 0.3963
F-statistic: 15.61 on 4 and 85 DF, p-value: 1.304e-09fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.17916 -0.79367 0.01258 0.69950 2.42938
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.96594 0.34808 -2.775 0.00675 **
femininity -0.01366 0.21735 -0.063 0.95004
damage 0.39961 0.04255 9.391 7.09e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9574 on 87 degrees of freedom
Multiple R-squared: 0.5063, Adjusted R-squared: 0.4949
F-statistic: 44.61 on 2 and 87 DF, p-value: 4.641e-14fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.20537 -0.78596 0.02543 0.67954 2.41385
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.9600444 0.3511835 -2.734 0.0076 **
femininity -0.0272028 0.2282872 -0.119 0.9054
damage 0.6865984 1.3978529 0.491 0.6245
damage:year -0.0001446 0.0007040 -0.205 0.8377
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9627 on 86 degrees of freedom
Multiple R-squared: 0.5065, Adjusted R-squared: 0.4893
F-statistic: 29.42 on 3 and 86 DF, p-value: 3.473e-13fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.22406 -0.75578 0.03644 0.66075 2.36687
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.94499 0.35224 -2.683 0.00875 **
femininity -0.04886 0.22991 -0.213 0.83222
damage 0.40709 0.04540 8.966 5.78e-14 ***
damage:post -0.01338 0.02742 -0.488 0.62675
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9616 on 86 degrees of freedom
Multiple R-squared: 0.5076, Adjusted R-squared: 0.4905
F-statistic: 29.56 on 3 and 86 DF, p-value: 3.152e-13fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.18995 -0.69177 0.03576 0.57306 2.33223
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.54721 0.51557 -1.061 0.291
femininity -0.71166 0.67075 -1.061 0.292
damage 0.34007 0.06882 4.941 3.79e-06 ***
femininity:damage 0.09623 0.08750 1.100 0.274
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9563 on 86 degrees of freedom
Multiple R-squared: 0.5131, Adjusted R-squared: 0.4961
F-statistic: 30.21 on 3 and 86 DF, p-value: 1.957e-13fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.19527 -0.69063 0.03283 0.57389 2.32960
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.484e-01 5.194e-01 -1.056 0.294
femininity -7.104e-01 6.754e-01 -1.052 0.296
damage 3.994e-01 1.422e+00 0.281 0.779
femininity:damage 9.568e-02 8.902e-02 1.075 0.286
damage:year -2.972e-05 7.115e-04 -0.042 0.967
Residual standard error: 0.9619 on 85 degrees of freedom
Multiple R-squared: 0.5131, Adjusted R-squared: 0.4902
F-statistic: 22.4 on 4 and 85 DF, p-value: 1.183e-12fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.22264 -0.69306 0.03572 0.57512 2.29001
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.548558 0.518221 -1.059 0.293
femininity -0.709614 0.674198 -1.053 0.296
damage 0.347975 0.072627 4.791 6.96e-06 ***
femininity:damage 0.092372 0.088607 1.042 0.300
damage:post -0.009873 0.027614 -0.358 0.722
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9612 on 85 degrees of freedom
Multiple R-squared: 0.5139, Adjusted R-squared: 0.491
F-statistic: 22.46 on 4 and 85 DF, p-value: 1.112e-12fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.14976 -0.73101 -0.05897 0.81375 2.11335
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.208e+00 2.683e-01 4.504 2.09e-05 ***
femininity 2.055e-02 3.692e-02 0.556 0.579
damage 7.666e-05 8.944e-06 8.571 3.68e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9933 on 86 degrees of freedom
Multiple R-squared: 0.462, Adjusted R-squared: 0.4495
F-statistic: 36.93 on 2 and 86 DF, p-value: 2.643e-12fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-1.85544 -0.68736 -0.01766 0.77156 2.14403
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.272e+00 2.745e-01 4.635 1.28e-05 ***
femininity 1.090e-02 3.795e-02 0.287 0.775
damage 8.084e-04 6.769e-04 1.194 0.236
damage:year -3.691e-07 3.414e-07 -1.081 0.283
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9923 on 85 degrees of freedom
Multiple R-squared: 0.4693, Adjusted R-squared: 0.4506
F-statistic: 25.06 on 3 and 85 DF, p-value: 1.038e-11fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-1.77676 -0.67869 -0.01185 0.77964 2.13707
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.295e+00 2.726e-01 4.749 8.20e-06 ***
femininity 7.509e-03 3.769e-02 0.199 0.843
damage 8.858e-05 1.195e-05 7.415 8.37e-11 ***
damage:post -2.240e-05 1.501e-05 -1.492 0.139
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9863 on 85 degrees of freedom
Multiple R-squared: 0.4758, Adjusted R-squared: 0.4573
F-statistic: 25.72 on 3 and 85 DF, p-value: 6.222e-12fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.33340 -0.68060 0.00043 0.78630 2.11302
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.387e+00 3.094e-01 4.484 2.27e-05 ***
femininity -7.930e-03 4.433e-02 -0.179 0.8584
damage 5.412e-05 2.145e-05 2.524 0.0135 *
femininity:damage 3.559e-06 3.080e-06 1.156 0.2511
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9913 on 85 degrees of freedom
Multiple R-squared: 0.4704, Adjusted R-squared: 0.4517
F-statistic: 25.16 on 3 and 85 DF, p-value: 9.576e-12fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.11242 -0.67395 0.00762 0.78451 2.13007
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.366e+00 3.142e-01 4.346 3.86e-05 ***
femininity -4.205e-03 4.524e-02 -0.093 0.926
damage 4.655e-04 8.767e-04 0.531 0.597
femininity:damage 2.427e-06 3.923e-06 0.619 0.538
damage:year -2.039e-07 4.344e-07 -0.469 0.640
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9959 on 84 degrees of freedom
Multiple R-squared: 0.4718, Adjusted R-squared: 0.4466
F-statistic: 18.75 on 4 and 84 DF, p-value: 4.76e-11fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-1.91453 -0.67436 0.01213 0.78673 2.13278
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.350e+00 3.116e-01 4.333 4.06e-05 ***
femininity -1.423e-03 4.480e-02 -0.032 0.9747
damage 7.761e-05 3.173e-05 2.446 0.0165 *
femininity:damage 1.402e-06 3.755e-06 0.373 0.7098
damage:post -1.847e-05 1.840e-05 -1.004 0.3181
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9913 on 84 degrees of freedom
Multiple R-squared: 0.4766, Adjusted R-squared: 0.4517
F-statistic: 19.13 on 4 and 84 DF, p-value: 3.25e-11fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.18319 -0.80666 0.02116 0.70095 2.43082
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.97661 0.39467 -2.474 0.0153 *
femininity 0.00383 0.03582 0.107 0.9151
damage 0.39586 0.04319 9.167 2.25e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9619 on 86 degrees of freedom
Multiple R-squared: 0.4955, Adjusted R-squared: 0.4838
F-statistic: 42.23 on 2 and 86 DF, p-value: 1.673e-13fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.19434 -0.79570 0.02475 0.69166 2.42442
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.9734750 0.3988579 -2.441 0.0167 *
femininity 0.0031126 0.0371051 0.084 0.9333
damage 0.5096226 1.4076632 0.362 0.7182
damage:year -0.0000573 0.0007087 -0.081 0.9357
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9675 on 85 degrees of freedom
Multiple R-squared: 0.4955, Adjusted R-squared: 0.4777
F-statistic: 27.83 on 3 and 85 DF, p-value: 1.24e-12fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.22032 -0.76742 0.04597 0.66709 2.38324
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.9580602 0.3999732 -2.395 0.0188 *
femininity 0.0005726 0.0371091 0.015 0.9877
damage 0.4015077 0.0461212 8.705 2.13e-13 ***
damage:post -0.0099008 0.0273423 -0.362 0.7182
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9668 on 85 degrees of freedom
Multiple R-squared: 0.4963, Adjusted R-squared: 0.4785
F-statistic: 27.91 on 3 and 85 DF, p-value: 1.166e-12fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.19218 -0.74921 0.00473 0.69324 2.32605
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.38241 0.78243 -0.489 0.62628
femininity -0.09512 0.11803 -0.806 0.42257
damage 0.31420 0.10239 3.069 0.00288 **
femininity:damage 0.01347 0.01531 0.880 0.38139
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9632 on 85 degrees of freedom
Multiple R-squared: 0.5, Adjusted R-squared: 0.4824
F-statistic: 28.34 on 3 and 85 DF, p-value: 8.501e-13fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.18677 -0.74892 0.00674 0.69932 2.32853
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.803e-01 7.890e-01 -0.482 0.631
femininity -9.538e-02 1.189e-01 -0.802 0.425
damage 2.579e-01 1.439e+00 0.179 0.858
femininity:damage 1.355e-02 1.555e-02 0.872 0.386
damage:year 2.809e-05 7.164e-04 0.039 0.969
Residual standard error: 0.9689 on 84 degrees of freedom
Multiple R-squared: 0.5001, Adjusted R-squared: 0.4762
F-statistic: 21 on 4 and 84 DF, p-value: 4.98e-12fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.21948 -0.73492 0.00601 0.66357 2.29420
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.388623 0.787083 -0.494 0.6228
femininity -0.094207 0.118730 -0.793 0.4298
damage 0.321146 0.106185 3.024 0.0033 **
femininity:damage 0.013017 0.015487 0.841 0.4030
damage:post -0.007362 0.027556 -0.267 0.7900
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9685 on 84 degrees of freedom
Multiple R-squared: 0.5005, Adjusted R-squared: 0.4767
F-statistic: 21.04 on 4 and 84 DF, p-value: 4.813e-12fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.15532 -0.72737 -0.04954 0.81079 2.12410
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.241e+00 1.973e-01 6.290 1.27e-08 ***
femininity 1.472e-01 2.245e-01 0.656 0.514
damage 7.659e-05 8.939e-06 8.567 3.74e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9926 on 86 degrees of freedom
Multiple R-squared: 0.4628, Adjusted R-squared: 0.4503
F-statistic: 37.04 on 2 and 86 DF, p-value: 2.49e-12fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-1.86798 -0.68709 -0.01967 0.75271 2.15566
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.282e+00 2.009e-01 6.380 8.87e-09 ***
femininity 8.926e-02 2.310e-01 0.386 0.700
damage 7.920e-04 6.770e-04 1.170 0.245
damage:year -3.608e-07 3.414e-07 -1.057 0.294
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9919 on 85 degrees of freedom
Multiple R-squared: 0.4698, Adjusted R-squared: 0.451
F-statistic: 25.1 on 3 and 85 DF, p-value: 1.005e-11fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-1.78785 -0.68656 -0.01926 0.76515 2.14785
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.299e+00 1.999e-01 6.497 5.30e-09 ***
femininity 6.580e-02 2.299e-01 0.286 0.775
damage 8.836e-05 1.197e-05 7.380 9.85e-11 ***
damage:post -2.206e-05 1.505e-05 -1.466 0.146
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.986 on 85 degrees of freedom
Multiple R-squared: 0.476, Adjusted R-squared: 0.4575
F-statistic: 25.74 on 3 and 85 DF, p-value: 6.093e-12fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.36341 -0.66899 0.01503 0.79200 2.12445
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.357e+00 2.177e-01 6.233 1.7e-08 ***
femininity -3.794e-02 2.692e-01 -0.141 0.888
damage 6.193e-05 1.481e-05 4.182 7.0e-05 ***
femininity:damage 2.297e-05 1.854e-05 1.239 0.219
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9895 on 85 degrees of freedom
Multiple R-squared: 0.4723, Adjusted R-squared: 0.4537
F-statistic: 25.36 on 3 and 85 DF, p-value: 8.202e-12fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.17707 -0.66814 0.00776 0.78772 2.13935
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.348e+00 2.197e-01 6.137 2.66e-08 ***
femininity -2.100e-02 2.737e-01 -0.077 0.939
damage 4.050e-04 8.503e-04 0.476 0.635
femininity:damage 1.745e-05 2.311e-05 0.755 0.452
damage:year -1.713e-07 4.244e-07 -0.404 0.688
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9944 on 84 degrees of freedom
Multiple R-squared: 0.4733, Adjusted R-squared: 0.4483
F-statistic: 18.87 on 4 and 84 DF, p-value: 4.208e-11fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-1.97716 -0.71205 0.01397 0.78739 2.14245
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.342e+00 2.184e-01 6.146 2.56e-08 ***
femininity -6.723e-03 2.715e-01 -0.025 0.98030
damage 7.834e-05 2.311e-05 3.390 0.00107 **
femininity:damage 1.137e-05 2.239e-05 0.508 0.61303
damage:post -1.688e-05 1.823e-05 -0.925 0.35736
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9903 on 84 degrees of freedom
Multiple R-squared: 0.4776, Adjusted R-squared: 0.4528
F-statistic: 19.2 on 4 and 84 DF, p-value: 3.007e-11fit <- lm(log(death + 1) ~ femininity + damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.17125 -0.81223 -0.00544 0.71008 2.44455
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.94425 0.35326 -2.673 0.00899 **
femininity -0.01888 0.21870 -0.086 0.93142
damage 0.39652 0.04334 9.150 2.44e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9619 on 86 degrees of freedom
Multiple R-squared: 0.4955, Adjusted R-squared: 0.4837
F-statistic: 42.23 on 2 and 86 DF, p-value: 1.676e-13fit <- lm(log(death + 1) ~ femininity + damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.18908 -0.81029 0.00002 0.69381 2.43344
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -9.415e-01 3.559e-01 -2.645 0.00972 **
femininity -2.760e-02 2.294e-01 -0.120 0.90454
damage 5.873e-01 1.426e+00 0.412 0.68153
damage:year -9.604e-05 7.177e-04 -0.134 0.89386
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9675 on 85 degrees of freedom
Multiple R-squared: 0.4956, Adjusted R-squared: 0.4778
F-statistic: 27.84 on 3 and 85 DF, p-value: 1.236e-12fit <- lm(log(death + 1) ~ femininity + damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity + damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.21218 -0.77634 0.01641 0.67109 2.38694
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.92900 0.35677 -2.604 0.0109 *
femininity -0.04923 0.23109 -0.213 0.8318
damage 0.40361 0.04663 8.655 2.7e-13 ***
damage:post -0.01184 0.02789 -0.425 0.6722
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9665 on 85 degrees of freedom
Multiple R-squared: 0.4965, Adjusted R-squared: 0.4788
F-statistic: 27.94 on 3 and 85 DF, p-value: 1.141e-12fit <- lm(log(death + 1) ~ femininity * damage + NULL, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + NULL, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.18416 -0.70048 0.01967 0.57862 2.34603
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.54721 0.51833 -1.056 0.294
femininity -0.69052 0.67815 -1.018 0.311
damage 0.34007 0.06919 4.915 4.27e-06 ***
femininity:damage 0.09283 0.08873 1.046 0.298
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9614 on 85 degrees of freedom
Multiple R-squared: 0.5019, Adjusted R-squared: 0.4843
F-statistic: 28.55 on 3 and 85 DF, p-value: 7.282e-13fit <- lm(log(death + 1) ~ femininity * damage + year:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + year:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.18379 -0.70048 0.01985 0.57851 2.34623
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.471e-01 5.223e-01 -1.048 0.298
femininity -6.906e-01 6.825e-01 -1.012 0.315
damage 3.361e-01 1.446e+00 0.232 0.817
femininity:damage 9.286e-02 9.003e-02 1.031 0.305
damage:year 1.995e-06 7.236e-04 0.003 0.998
Residual standard error: 0.9671 on 84 degrees of freedom
Multiple R-squared: 0.5019, Adjusted R-squared: 0.4782
F-statistic: 21.16 on 4 and 84 DF, p-value: 4.285e-12fit <- lm(log(death + 1) ~ femininity * damage + post:damage, data = df)
summary(fit)
Call:
lm(formula = log(death + 1) ~ femininity * damage + post:damage,
data = df)
Residuals:
Min 1Q Median 3Q Max
-2.21457 -0.70756 0.02929 0.58329 2.30581
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.548427 0.521109 -1.052 0.296
femininity -0.691889 0.681779 -1.015 0.313
damage 0.347208 0.073099 4.750 8.3e-06 ***
femininity:damage 0.089858 0.089685 1.002 0.319
damage:post -0.008916 0.028039 -0.318 0.751
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9665 on 84 degrees of freedom
Multiple R-squared: 0.5025, Adjusted R-squared: 0.4788
F-statistic: 21.21 on 4 and 84 DF, p-value: 4.079e-12fit <- lm(log(death + 1) ~
branch(main_interaction,
"main" ~ femininity + damage,
# "with_damage_and_z3" ~ femininity * damage + femininity * z3,
# "with_damage_and_zcat" ~ femininity * damage + femininity * zcat,
# "with_damage_and_zwind" ~ femininity * damage + femininity * zwind,
# "with_damage_and_zpressure" ~ femininity * damage + femininity * zpressure,
"with_damage" ~ femininity * damage
) + branch(control_year, "none" ~ NULL, "year_x_damage" ~ year:damage, "post1979_x_damage" ~ post:damage),
data = df)
summary(fit)We first visualise the model’s coefficients as confidence intervals:
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() + geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high,
color = (p.value < 0.05))) + theme_minimal() + labs(y = "Coefficient", x = "Meanpointestimateand95%ConfidenceInterval")
broom::tidy(fit, conf.int = TRUE) %>%
ggplot() +
geom_pointrange(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high, color = (p.value < 0.05))) +
theme_minimal() +
labs(y ="Coefficient", x = "Mean point estimate and 95% Confidence Interval")Next, we predict the expected number of deaths and a 50% prediction interval as a function of the femininity of the name
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() + ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() + theme_minimal()
data_grid(df, femininity, damage, nesting(post, year)) %>%
broom::augment(fit, newdata = .) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
ggplot() +
ggdist::stat_lineribbon(aes(x = femininity, y = .fitted), .width = c(0.5)) +
scale_fill_brewer() +
theme_minimal()Execution and Results
Next, we attempt to make sense of the multiverse analysis as a whole,
using a specification curve. We first create a new datastructure
new_hurricane_data, and estimate the average expected
number of deaths and standard error. To make comparable point estimates
for the continuous and discrete measures of femininity, we
compute the average value of the former for the two possible values of
the latter, and compute as the effect size the difference in predicted
deaths for both values. Thus, mean_deaths are marginal
effects computed at sample means.
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = identity(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df, femininity = masfem_levels, damage = c(2000, 4000),
nesting(post, year)) %>%
mutate(damage = log(damage))
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)# used for multiverse vis
masfem_levels = summarise(group_by(df, female), mean = mean(femininity))$mean
new_hurricane_data = data_grid(df,
femininity = masfem_levels,
damage = c(2000, 4000),
# nesting(zcat, zwind, zpressure, z3),
nesting(post, year)) %>%
mutate(
damage = branch(damage_transform,
"no_transform" ~ identity(damage),
"log_transform" ~ log(damage)
)
)
model.coef = broom::tidy(fit, conf.int = TRUE) %>%
filter(term == "femininity")
# aggregate fitted effect of female storm name
expectation = new_hurricane_data %>%
broom::augment(fit, newdata = ., se_fit = TRUE) %>%
mutate(.fitted = exp(.fitted) - 1) %>%
mutate(.id = row_number(), femininity = as.numeric(factor(femininity)) - 1) %>%
group_by(femininity) %>%
summarise(.fitted = mean(.fitted), .se = mean(.se.fit), .groups = "drop_last") %>%
pivot_wider(names_from = femininity, values_from = c(.fitted, .se)) %>%
mutate(mean_deaths = .fitted_1 - .fitted_0, .se = sqrt(.se_1^2 + .se_0^2)) %>%
select(mean_deaths, .se)
deg_freedom.model = df.residual(fit)After we’ve specified our multiverse analysis, we would like to execute the entire multiverse, and view the results.
execute_multiverse(M)Below, we plot the mean difference point estimate for expected deaths
when a hurricane has a more feminine name, for each unique analysis
path. We find that based on these arbitrary specifications of the
multiverse, there is perhaps no relation between femininity
of the name of a hurricane and the number of deaths that it causes, as
some models predict a lower number of deaths, and some predict much
higher.
data.spec_curve = extract_variables(M, expectation, model.coef, deg_freedom.model) %>%
unnest(c(expectation, model.coef)) %>%
select( .universe, !! names(parameters(M)), mean_deaths, estimate, .se, p.value, deg_freedom.model) %>%
arrange( mean_deaths ) %>%
mutate(
.universe = 1:nrow(.),
effect = ifelse(p.value < 0.05, ifelse(estimate < 0, "negative", "positive"), "not significant")
)
p1 <- data.spec_curve %>%
gather( "parameter_name", "parameter_option", !! names(parameters(M)) ) %>%
mutate( parameter_name = factor(stringr::str_replace(parameter_name, "_", "\n")) ) %>%
ggplot() +
geom_point( aes(x = .universe, y = parameter_option, color = effect), size = 1 ) +
labs( x = "universe #", y = "option included in the analysis specification") +
facet_grid(parameter_name ~ ., space="free_y", scales="free_y", switch="y")+
scale_colour_manual(values=c("#FF684B", "#999999", "#6E52EB")) +
theme_minimal() +
theme(strip.placement = "outside",
strip.background = element_rect(fill=NA,colour=NA),
panel.spacing.x=unit(0.15,"cm"),
strip.text.y = element_text(angle = 180, face="bold", size=10),
panel.spacing = unit(0.25, "lines")
)
p2 <- data.spec_curve %>%
mutate(
conf.low = purrr::pmap_dbl(list(mean_deaths, .se, deg_freedom.model), ~ gamlss.dist::qTF(0.025, ..1, ..2, ..3)),
conf.high = purrr::pmap_dbl(list(mean_deaths, .se, deg_freedom.model), ~ gamlss.dist::qTF(0.975, ..1, ..2, ..3))
) %>%
ggplot() +
ggdist::geom_pointinterval(aes(x = .universe, y = mean_deaths, ymin = conf.low, ymax = conf.high, color = effect)) +
labs(x = "", y = "effect size") +
theme_minimal() +
scale_colour_manual(values=c("#FF684B", "#999999", "#6E52EB"))
cowplot::plot_grid(p2, p1, axis = "bltr", align = "v", ncol = 1, rel_heights = c(1, 3))